Parameterization before Meta-Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering
Round 1
Reviewer 1 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsThe paper has been substantially improved and now it is publishable.
Author Response
Comments and Suggestions for AuthorsThe paper has been substantially improved and now it is publishable.
Response:Thank you very much for recognizing our research work. We have further refined the manuscript, with the revisions highlighted in blue. The revised document is attached for your review.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (Previous Reviewer 2)
Comments and Suggestions for AuthorsThank you for your contribution to AI-based parameterization for forest ecology.
Author Response
Comments and Suggestions for AuthorsThank you for your contribution to AI-based parameterization for forest ecology.
Response:Thank you very much for recognizing our research work. We have further refined the manuscript, with the revisions highlighted in blue. The revised document is attached for your review.
Author Response File: Author Response.pdf
Reviewer 3 Report (Previous Reviewer 3)
Comments and Suggestions for AuthorsSome advice and suggestions provided here section by section:
- Abstract:
Advantages:
1. The abstract effectively summarizes the study's core concepts, including the context, methodology, and outcomes, providing a snapshot of the research for the reader.
Disadvantages:
1. Although the abstract mentions that the method is a powerful tool for scientific research, more specifics about practical applications or implications for the field could enhance relevance.
Comments and questions to improve:
1. How can you ensure the importance and novelty of your research are communicated effectively? What unique insights does your study bring to the field?
- Introduction:
Advantages:
1. Provides a comprehensive overview of the importance of image data in nature reserves. Highlights the challenges faced in managing and analyzing large volumes of image data.
2. Introduces the proposed novel method, Cross-Modal Embedding Clustering (CMEC), to address the identified challenges.
3. It emphasizes the need for interdisciplinary integration, highlighting its relevance in the larger context of forest management and ecology.
Disadvantages:
1. Lack of a clear thesis statement.
2. The use of specialized terminology (e.g., "cross-modal retrieval," "dual-encoder architecture") may alienate readers who are not experts in the field. Some of these terms should be defined or simplified.
Comments and questions to improve:
1. What is the main research question you aim to answer with this study? A clear statement of the problem might help focus the introduction.
2. Could you provide a brief example or case study to illustrate the limitations of current approaches to image annotation in the ecological context?
- Related Works:
Advantages:
1. Introduces the emerging trend of AI-assisted meta-analysis and its potential benefits for efficiency and data integration.
2. The section provides a solid foundation for the authors' proposed system, demonstrating their understanding of the relevant literature and limitations.
Disadvantages:
1. The section could benefit from more critical analysis and synthesis of the literature.
2. The use of specialized terms, like "heterogeneity issues" and "chain-of-thought techniques," may confuse readers who are not familiar with these concepts. Definitions or explanations could be provided to enhance clarity.
Comments and questions to improve:
1. How can you enhance the organization of the Related Works section to guide readers through the main themes and discussions effectively? Can you provide a summary table!
2. How might you address the technical challenges mentioned in meta-analyses, such as handling high research heterogeneity and spatial/temporal scale limitations?
- Preliminary and Methods:
Advantages:
1. It illustrates how multiple methodologies (like AI, deep learning, and cross-modal retrieval) have been integrated to address the research problem, showcasing the interdisciplinary nature of the work.
Disadvantages:
1. Certain technical terms and methodologies (like "momentum encoders" and "contrastive learning") are mentioned but not well-defined. Clear definitions would aid comprehension.
Comments and questions to improve:
1. Can you define and explain specialized terms and acronyms (e.g., "momentum encoders," "contrastive learning") clearly to ensure that all readers can understand the methods described?
2. What key points from the figures and tables referenced can be summarized in the text to improve the connection between the visuals and the written content?
3. How do you plan to validate the effectiveness of the proposed method? Is there a more detailed discussion of your validation process included elsewhere in the manuscript?
Experiments and Results Analysis:
Advantages:
1. The section balances quantitative metrics (like accuracy percentages) with qualitative descriptions of the model's performance, providing a well-rounded view of its capabilities.
Disadvantages:
1. While numerous tables and figures are referenced to support the analysis, the section could provide more interpretative commentary on these visuals to guide the reader through the implications of the data presented.
2. Although the experimental results are discussed, the significance of these results in the context of existing literature or advancements in the field is somewhat lacking. Elaborating on how the findings contribute to the current understanding or practices could strengthen the argument.
Comments and questions to improve:
1. Could you enhance the discussion about how the various components of the model interact? How does each module contribute to the overall effectiveness of the system in achieving its goals?
2. How can the limitations of your study be discussed more thoroughly? What are the implications of these limitations for the generalizability of your results?
3. What additional insights can you provide regarding the practical applications of your findings? How might the model be adapted or refined in future studies?
Discussion:
Advantages:
1. This section effectively highlights key limitations of the study, which is essential for assessing the robustness and applicability of the findings.
2. It outlines potential avenues for future research, indicating a forward-thinking approach that can guide subsequent studies in the field.
Disadvantages:
1. The section is brief and does not deeply analyze the findings or their implications. A more comprehensive exploration of the results and their relevance to existing literature could enhance quality.
2. Some content may overlap with sections of the paper (like limitations), which can make it feel repetitive rather than novel or unique to the discussion.
3. There is little mention of how the findings might impact practical stakeholders, such as conservationists, ecologists, or policymakers. Incorporating these perspectives could increase the relevance of the discussion.
Comments and questions to improve:
1. How can you incorporate perspectives from stakeholders in forestry ecology, such as practitioners or policymakers, in your discussion? What implications do your findings have for these groups?
2. Can you clarify how the planned improvements (like online learning) tie back to the limitations of the current model? What changes will these developments bring to the model's performance or applicability?
3. What statistical or empirical evidence can you provide to support your claims about the need for further research in meta-learning and online feedback systems?
Conclusions:
Advantages:
1. By outlining potential future research directions, the section helps position the study within the ongoing discourse and suggests pathways for further investigation.
Disadvantages:
1. Some statements in the Conclusions are repetitive of ideas discussed in previous sections, which can dilute the impact of the conclusions. Ensuring original content could enhance the effectiveness of this section.
Comments and questions to improve:
1. In what ways can you connect the conclusions drawn from your findings to broader trends or issues in the field of ecology? What overarching theme should readers consider?
Thank you for considering my opinion.
Comments on the Quality of English LanguageEnglish language is fine. But minor editing of English language upgrades your work.
Author Response
Some advice and suggestions provided here section by section:
- Abstract:
Advantages:
The abstract effectively summarizes the study's core concepts, including the context, methodology, and outcomes, providing a snapshot of the research for the reader.
Disadvantages:
Although the abstract mentions that the method is a powerful tool for scientific research, more specifics about practical applications or implications for the field could enhance relevance.
Comments and questions to improve:
Comment 1: How can you ensure the importance and novelty of your research are communicated effectively? What unique insights does your study bring to the field?
Response:Forestry ecology heavily depends on scale effects, with scientific conclusions differing in significance depending on the spatial and temporal scales of the research. As these scales increase, the volume of aggregated data also grows, while the time and resources of researchers remain limited. Our proposed method aims to provide an intelligent research assistant tool that leverages large-scale data, assisting forestry ecology researchers in conducting studies more efficiently across larger temporal and spatial scales. Following your suggestion, we have revised the end of the abstract, with the changes highlighted in blue font.
Introduction:
Advantages:
Provides a comprehensive overview of the importance of image data in nature reserves. Highlights the challenges faced in managing and analyzing large volumes of image data.
Introduces the proposed novel method, Cross-Modal Embedding Clustering (CMEC), to address the identified challenges.
It emphasizes the need for interdisciplinary integration, highlighting its relevance in the larger context of forest management and ecology.
Disadvantages:
Lack of a clear thesis statement.
The use of specialized terminology (e.g., "cross-modal retrieval," "dual-encoder architecture") may alienate readers who are not experts in the field. Some of these terms should be defined or simplified.
Comments and questions to improve:
Comment 2: What is the main research question you aim to answer with this study? A clear statement of the problem might help focus the introduction.
Response:
In the Introduction section, we have added an illustration at the beginning to clarify the existing methods for generating language descriptions based on input images. We then explicitly introduce our scientific problem by aligning it with our application needs and proposed methods. The corresponding modifications are highlighted in blue font.
Comment 3: Could you provide a brief example or case study to illustrate the limitations of current approaches to image annotation in the ecological context?
Response:We have added Figure to visually present examples of image captioning, a deep learning model designed to generate textual descriptions for images, and to elaborate on the limitations of applying this cross-modal approach in the field of forestry ecology.
Related Works:
Advantages: Introduces the emerging trend of AI-assisted meta-analysis and its potential benefits for efficiency and data integration.
The section provides a solid foundation for the authors' proposed system, demonstrating their understanding of the relevant literature and limitations.
Disadvantages:
The section could benefit from more critical analysis and synthesis of the literature.
The use of specialized terms, like "heterogeneity issues" and "chain-of-thought techniques," may confuse readers who are not familiar with these concepts. Definitions or explanations could be provided to enhance clarity.
Comments and questions to improve:
Comment 4: How can you enhance the organization of the Related Works section to guide readers through the main themes and discussions effectively? Can you provide a summary table!
Response:We have added a table listing representative literature at key technological milestones closely related to our proposed method. The corresponding explanatory text is highlighted in blue.
Comment 5:The use of specialized terms, like "heterogeneity issues" and "chain-of-thought techniques," may confuse readers who are not familiar with these concepts. Definitions or explanations could be provided to enhance clarity.
Response:We have provided annotations for the specialized terms you mentioned, with the corresponding modifications highlighted in blue within the text.
Comment 6:How might you address the technical challenges mentioned in meta-analyses, such as handling high research heterogeneity and spatial/temporal scale limitations?
Response: I will address your question from three perspectives: data structure}, vectorization}, and parameterization}. Structured} data refers to data organized into an ordered format, typically presented in tables, with well-defined relationships that facilitate storage, transmission, and computation. In contrast, unstructured} data is scattered, lacks predefined associations, and cannot be expressed using fixed logical structures. For such data, manual organization according to specific needs can be employed. When data scales beyond manual processing capabilities, automated handling using deep learning models is commonly used. Deep learning is a statistical tool that uncovers inherent data relationships, provided that the target data is computable by a computer.
Our proposed method involves projecting data from various sources and formats into a high-dimensional vector} space for computation. This projection is performed by an encoder component, trained on a sufficiently large and labeled dataset. In our study, we use an image-text pair dataset (where each image-text sample consists of an image and its corresponding descriptive text) to train a pair of encoders: one for vectorizing} image data and another for vectorizing} textual data. Consequently, images and texts are projected into the same vector} space, where they can be processed by a question-answering inference model. We use a natural language generation model for this purpose, referred to as a decoder within the model architecture, which 'translates' the vector} computations (understood here as statistical relationships) into natural language for user readability.
Broadly, this approach allows any heterogeneous data to be projected into a shared vector} space for resolution, thus addressing the issue of data heterogeneity. Once a deep learning model has learned the statistical patterns in the data, it can handle larger-scale datasets, with the data relationships embedded in the model's parameters, which is the basis for the term 'Parameterization}' in the title.
Regarding the spatial/temporal scale, in terms of time, we can input image and textual data spanning longer time periods into the model, and even data from nationwide or global natural conservation maps, such as those from the Wildlife Conservation Society dataset (https://library.wcs.org/Library/Science-Data/Datasets.aspx), which includes conservation map images from across Eurasia, the Americas, and beyond. Ecological research heavily relies on scale, but researchers' time and effort are limited. Manual meta-analyses are thus constrained by time and spatial scale. Automated processing of such spatiotemporal data by intelligent models overcomes these limitations. The next step involves delivering the information and knowledge learned by the model to users through image-text question-answering, leveraging the model’s reasoning capability. This approach allows researchers to query the model, maintaining coherence without the interruptions typical in web searches, where researchers frequently pause to organize results and decide on the next search term.Practical application involves issues of data collection and organization, as well as further model adjustment. Once an iterative feedback loop is established between intelligent algorithms and data collection, a Matthew effect can emerge in the field, where higher-quality models attract more users, and increased user engagement generates more data for further model refinement. I am deeply honored to receive your guidance on academic writing and inspiration on scientific issues. Due to my limited expertise, my expressions may inevitably contain inaccuracies, and I welcome your corrections. We have condensed this response and incorporated it into the manuscript, highlighted in blue. You can navigate to the revisions via the hyperlinks within the attached manuscript.
Preliminary and Methods:
Advantages: It illustrates how multiple methodologies (like AI, deep learning, and cross-modal retrieval) have been integrated to address the research problem, showcasing the interdisciplinary nature of the work.
Disadvantages: Certain technical terms and methodologies (like "momentum encoders" and "contrastive learning") are mentioned but not well-defined. Clear definitions would aid comprehension.
Comments and questions to improve:
Comment 7:Can you define and explain specialized terms and acronyms (e.g., "momentum encoders," "contrastive learning") clearly to ensure that all readers can understand the methods described?
Response:We have provided explanations for the relevant technical terms in the context, with corresponding revisions highlighted in blue font.
Comment 8: What key points from the figures and tables referenced can be summarized in the text to improve the connection between the visuals and the written content?
Response:We added a summary of the figures and tables related to the proposed method at the beginning of the Preliminary and Methods section, with the corresponding text highlighted in blue.
Comment 9: How do you plan to validate the effectiveness of the proposed method? Is there a more detailed discussion of your validation process included elsewhere in the manuscript?
Response:We added a paragraph at the beginning of the experimental section explaining how the effectiveness of the proposed method is validated, with the corresponding text highlighted in blue.
Experiments and Results Analysis:
Advantages:The section balances quantitative metrics (like accuracy percentages) with qualitative descriptions of the model's performance, providing a well-rounded view of its capabilities.
Disadvantages:
While numerous tables and figures are referenced to support the analysis, the section could provide more interpretative commentary on these visuals to guide the reader through the implications of the data presented.
Although the experimental results are discussed, the significance of these results in the context of existing literature or advancements in the field is somewhat lacking. Elaborating on how the findings contribute to the current understanding or practices could strengthen the argument.
Comments and questions to improve:
Comment 10:Could you enhance the discussion about how the various components of the model interact? How does each module contribute to the overall effectiveness of the system in achieving its goals?
Response:We have integrated the contributions of the relevant modules to the overall system into the introductory paragraphs of the Experiment section.
Comment 11: How can the limitations of your study be discussed more thoroughly? What are the implications of these limitations for the generalizability of your results?
Response:When training deep models, it is common to define specific tasks, such as designing an image recognition task for image recognition, to constrain the model in establishing a certain input-output relationship. Nevertheless, classification remains fundamental to model intelligence. In the context of forestry ecology question-answering involving images and text, the basis is also how classification is performed. Our proposed method focuses on clustering based on factual information within the images. This classification criterion is not predetermined but naturally formed by the relevance within the data itself.
A primary limitation of our proposed multi-task perspective approach is that different tasks exhibit varying degrees of data fitting. Specifically, the model may overfit on one task while underfitting on others. If a task directs the model to learn noise rather than features, it can further exacerbate model convergence issues. The balancing of multiple tasks relies on empirical adjustments of a limited set of hyperparameters, which further increases model uncertainty. Although image recognition and cross-modal retrieval are fundamental functionalities for question-answering models, our optimization is tailored specifically for question-answering tasks. Consequently, the cross-modal representation embeddings obtained may not generalize to all recognition and retrieval tasks. For question-answering tasks, our constructed dataset is oriented towards images and texts in the forestry ecology domain, lacking generalization for common-sense knowledge question-answering.
Comment 12: What additional insights can you provide regarding the practical applications of your findings? How might the model be adapted or refined in future studies?
Response:Our method requires a substantial amount of data for training or fine-tuning, and optimizing the model through online data collection can result in significant delays. A key area of future research will be to optimize the model with less data and establish a seamless data collection and model upgrading feedback loop in applications. We have added relevant content to the Discussion section, highlighted in blue.
Discussion:
Advantages:
This section effectively highlights key limitations of the study, which is essential for assessing the robustness and applicability of the findings.
It outlines potential avenues for future research, indicating a forward-thinking approach that can guide subsequent studies in the field.
Disadvantages:
The section is brief and does not deeply analyze the findings or their implications. A more comprehensive exploration of the results and their relevance to existing literature could enhance quality.
Some content may overlap with sections of the paper (like limitations), which can make it feel repetitive rather than novel or unique to the discussion.
There is little mention of how the findings might impact practical stakeholders, such as conservationists, ecologists, or policymakers. Incorporating these perspectives could increase the relevance of the discussion.
Comments and questions to improve:
Comment 13:How can you incorporate perspectives from stakeholders in forestry ecology, such as practitioners or policymakers, in your discussion? What implications do your findings have for these groups?
Response:The interdisciplinary nature of the model provides an advanced decision-support tool for forestry practitioners and policymakers. Practitioners can extract relevant insights for sustainable forestry management, while policymakers benefit from evidence-based recommendations derived from complex ecological datasets. The integration of deep learning models with traditional ecological theories elevates the accuracy of ecological modeling. The model could be extended to estimate the value of ecosystem services (e.g., carbon sequestration, water filtration) by mapping forest health and ecosystem functions across diverse temporal and spatial scales. This is critical for establishing economic incentives for conservation.The existing model relies on static datasets, meaning it cannot dynamically update based on new data or user feedback after training. This makes it less adaptive to evolving ecological patterns, new research findings, or shifts in user information needs. Online learning allows the model to continuously refine its parameters in response to real-time data and interactions. This enables the model to stay current with the latest ecological data and scientific literature, making its predictions and retrievals more relevant and timely. For example, if new satellite imagery shows sudden changes in forest cover, the model can quickly adjust its outputs to reflect these updates.The model may be prone to biases, especially if the training data is skewed toward specific regions, species, or ecological conditions. This limits its generalizability across diverse ecosystems and climates. By incorporating online learning, the model can gradually incorporate diverse datasets over time, learning from new data sources as they become available. This helps mitigate initial biases by ensuring the model is exposed to a broader range of ecological conditions, leading to improved generalization across different ecosystems. The static nature of the current model may not fully align with the dynamic needs of practitioners and policymakers, whose decisions are influenced by changing environmental, economic, and social conditions. With the ability to learn and adjust in real time, the model can better support real-world decision-making. Practitioners can interact with the model, feeding it new information or datasets specific to their local context, which allows the model to generate more customized and actionable insights. This is particularly useful in contexts like forest management, where conditions can change rapidly due to factors like climate variability or deforestation. The model's predictions are based on pre-trained patterns and may struggle to adapt to unseen or novel ecological phenomena, especially in regions with limited historical data. Online learning will enhance the model's predictive capabilities by allowing it to adapt to novel data in real-time. For instance, when faced with unfamiliar forest conditions due to unexpected environmental changes, the model can quickly adjust its predictions as new data streams in. This leads to more accurate forecasts of forest dynamics, species distributions, and biodiversity changes. The model lacks a mechanism to incorporate user feedback, meaning any misclassifications or irrelevant results cannot be corrected through interaction. The integration of reinforcement learning or other user-centered feedback mechanisms will allow the model to learn from user interactions. This creates a feedback loop where users can correct or guide the model’s responses, improving the accuracy of future interactions. Over time, this leads to a more personalized and responsive model that better caters to the specific needs of different stakeholders, whether they are researchers, forest managers, or policymakers. In the domain of forestry ecology, where environmental conditions change (e.g., due to climate change or deforestation), models that adapt to new data through meta-learning can provide more reliable and up-to-date predictions, supporting the need for further research in this direction.
Comment 14:Can you clarify how the planned improvements (like online learning) tie back to the limitations of the current model? What changes will these developments bring to the model's performance or applicability?
Response:First, we believe that the ability of intelligent models to accumulate data through application usage is more important than their initial performance. In other words, it is acceptable for intelligent models to exhibit suboptimal performance in the early stages as long as there are users, since data acquisition is critical. These online application data are real-world occurrences, reflecting both user behavior and model characteristics, and serve as crucial references for guiding subsequent model iterations. The aggregation of online application data from all users for model fine-tuning can be seen as the model learning more knowledge through use. However, each user has different needs, and as a result, the intelligent assistant model must generate personalized answers for each user, which involves fine-tuning. Yet, fine-tuning based on global data often conflicts with personalized fine-tuning for individual users, representing a tradeoff between local and global optimization.
In this research direction, we are exploring reinforcement learning methods to allow the model to learn users' online reasoning processes. However, as the number of users increases, the number of models requiring fine-tuning also grows, making the computational cost unsustainable for practical application. Therefore, we are investigating whether a single model can adapt to all users, rather than assigning a separate model to each user. In this line of research, we have chosen meta-learning approaches. Fundamentally, everything discussed revolves around the model's ability to accumulate data through application, while reinforcement learning and meta-learning are methods for processing this data. Once a feedback loop is established between data accumulation and intelligent upgrading, the model's performance will inevitably improve, a concept we refer to as 'data intelligence.' We have updated the Discussion section according to your suggestions and marked the changes in blue.
Comment 15:What statistical or empirical evidence can you provide to support your claims about the need for further research in meta-learning and online feedback systems?
Response:First, different users may have varying needs for the same question, while after convergence, models tend to provide similar answers to repeated queries on the same question, with differences only in expression. This necessitates that models not only deliver stable statistical results but also generate personalized responses. Meta-learning research holds promise in this regard for achieving breakthroughs. Second, deep learning models are data-hungry, and their ability to continuously accumulate and digest data during application is more crucial than mere performance. Put simply, the ability to continuously acquire data and self-upgrade is essential for the sustainability of intelligent models. From the perspective of practical applications, this can be divided into two levels: macro and micro. The model must not only produce overall statistical results across the entire dataset but also adapt to different application scenarios. This brings us back to meta-learning, which we have identified as a key research direction for question-answering search engines. We have updated the Discussion section according to your suggestions and highlighted the changes in blue.
Conclusions:
Advantages: By outlining potential future research directions, the section helps position the study within the ongoing discourse and suggests pathways for further investigation.
Disadvantages:Some statements in the Conclusions are repetitive of ideas discussed in previous sections, which can dilute the impact of the conclusions. Ensuring original content could enhance the effectiveness of this section.
Comments and questions to improve:
Comment 16:In what ways can you connect the conclusions drawn from your findings to broader trends or issues in the field of ecology? What overarching theme should readers consider?
Response:Based on your suggestion, I understand that the conclusion should not only distill the essence of the paper but also highlight the trends and future directions of the field, leaving readers with valuable insights and inspiration. With this understanding, I have rewritten the conclusion accordingly.
Author Response File: Author Response.pdf
Reviewer 4 Report (Previous Reviewer 5)
Comments and Suggestions for AuthorsThe manuscript has been improved, so there are some minor notes, for example, Figure 4 needs to be repositioned so that the image will be seen more clearly.
Related works and the theoretical aspect of this research should challenge existing views or offer new insights into the topic. For example, theoretical debate should also not be lost at the end or still appear in the discussion section. It might be helpful to explain how these ideas relate to any aims that can be developed from these discussions and link to the limitation of this study.
The discussion section should begin with a reiteration of the purpose of the study to guide the reader as to what the paper is about. The discussion in the paper can be strengthened by considering the relevance of these findings to existing literature.
Author Response
Comment 1: The manuscript has been improved, so there are some minor notes, for example, Figure 4 needs to be repositioned so that the image will be seen more clearly.
Response:We have enlarged the image in the center of Figure.
Comment 2:Related works and the theoretical aspect of this research should challenge existing views or offer new insights into the topic. For example, theoretical debate should also not be lost at the end or still appear in the discussion section. It might be helpful to explain how these ideas relate to any aims that can be developed from these discussions and link to the limitation of this study.
Response:We have added Table\ref{tab:Reworks} and corresponding explanations in the Related Work section, making it easier to compare the pros and cons of related work. We have also made modifications to the Discussion section based on your suggestions. The corresponding modified parts in the manuscript are displayed in blue font.
Comment 3:The discussion section should begin with a reiteration of the purpose of the study to guide the reader as to what the paper is about. The discussion in the paper can be strengthened by considering the relevance of these findings to existing literature.
Response: We have made corresponding modifications to the Discussion section\ref{sec:Discus} based on your suggestions.
We have further refined the manuscript, with the revisions highlighted in blue. The revised document is attached for your review.
Author Response File: Author Response.pdf
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsRoughly speaking, meta-analysis is the statistical combination of results from
two or more separate studies. These days, most results of various studies are
freely available but in most cases they are not peer-reviewed and they cannot
and should not be trusted. Chat-GPT and other related systems use data that,
in most cases, are not peer-reviewed or ae results of (poor?) plagiarism.
Therefore, one needs to use tools that employ data that can be trusted.
The authors claim that "Small models trained from scratch typically exhibit
significant performance gaps compared to pre-trained large models." This is
true, nevertheless, this is a drill that one must do in order to ensure that
teh results of meta-analysis are useful.
The authors have usd ChatGPT-2 (a rather old version of ChatGPT) to generate something
useful for orest Ecology. Here is their description of what they have done:
>We utilized the iNaturalist2017 dataset [15], which consists of 5089 species, with 579,184
>training set images and 95,986 validation set images. However, the dataset does not include
>text descriptions corresponding to the images. In order to generate text descriptions
>paired with images we followed the pipeline of Laion COCO 600M [16] to curate our Nature
>Conservation Image-text Pair Dataset (NaCID) in four steps: 1) using BLIP L/14 to generate
>40 captions for each image in iNaturalist dataset; 2) ranking them using Open AI CLIP
>L/14 to select the best 5 captions ; 3) using Open AI RN50x64 CLIP model to select the best
>one; 4) using a small, fine-tuned T0 [17] model to roughly repair grammar and punctuation
>of the texts
It is obvious that here he authors performed some form of "text processing" to pair data.
However, what is missing is a proof or at least a validation that the rsulting data are
indeed correct.
Figure 7, summarizes what these authors have done. In this respect, the paper is not
original as they utilize known tools and mehodologies to create useful dat for forrests.
This is not the first time someone uses known tools and methods to create something
that can be proved useful. However, it has to be as much complete as possible. It seems
that here the authors have done so.
My general opinion is that the authors know what they talkning about and have indeed used the
tools described. It would be nice to make their work open and available to anyone who migt
like to use it. In adition, it would be useful to write some sort of script or a step-by-step
recipe that can be used to replicate their work. In addition, I have to stress that I have not
found any problems in understanding what they want to say.
The paper does not contain any contradictory arguments and the conclusions are in accordance with
what is described in the body of the text. I think the paper can be published after the authors\
have addressd the few points made in this report.
A few typos and errors that can be detected after a careful read.
Author Response
Thank you very much for your valuable suggestions and generous assistance. Please allow me to respond from the perspectives of dataset validation and Figure 7.
Comment 1: It is obvious that here the authors performed some form of "text processing" to pair data. However, what is missing is a proof or at least a validation that the resulting data are indeed correct.
Response: This dataset is an improvement based on the public dataset INaturalist2017, tailored for cross-modal alignment of images and text in the field of forestry ecology. The alignment effectiveness is validated in Section 4.2 of the experimental part, as evidenced by the results shown in Table 4. Moreover, the cross-modal alignment of species image features with forestry ecology knowledge is discussed in detail in my previous work (https://doi.org/10.3390/s24103130). I am particularly grateful for your professional and rigorous academic suggestions.
Comment 2: Figure 7 summarizes what these authors have done. In this respect, the paper is not original as they utilize known tools and methodologies to create useful data for forests. This is not the first time someone uses known tools and methods to create something that can be proved useful. However, it has to be as complete as possible. It seems that here the authors have done so.
Response: To achieve the results in Figure 7, we conducted fine-grained species image recognition, cross-modal alignment of species image features with forestry ecology literature knowledge, and training of the model's reasoning capabilities. In these tasks, we improved the cross-modal encoder, allowing the image encoder to achieve higher recognition accuracy without being constrained by predefined categories. For cross-modal alignment, we proposed a shared space embedding method centered around factual information within images, which mitigates linguistic ambiguity and enhances the robustness of cross-modal representations, as validated in Section 4.2. Building on existing cross-modal question-answering research, we designed a decomposition of question logic, which facilitates the model's understanding of professional knowledge interpretation of image content. These incremental improvements on the foundation of previous research have yielded the experimental results shown in Figure 7. As you rightly pointed out, we are indeed standing on the shoulders of giants to carry out this research.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsLn.27 The question does not reflect the Figure. Please revise it as ‘Can an AI model learn from a literature collection and engage in question-and-answer format?’
Ln. 30 The statement 'Researchers can continue questioning, maintaining the coherence of their thoughts until they arrive at satisfactory scientific questions' does not align with the Figure. If the researchers are involved in ‘problem definition,’ there should be another arrow from the last step back to ‘the problem definition’. Either the statement or Figure 1 should be revised to match better each other. This will not only improve the accuracy of the information presented but also enhance the overall quality of the work.
Ln. 61 Insert a new paragraph to guide the reader through the rest of the paper, highlighting the general organisation of the following sections.
The introduction section contains only two references. More information and discussion are needed to help the reader understand the research background, stimulate their interest, and explain why the research paper is worth reading. I strongly suggest revising the paragraphs in the introduction.
Ln. 64 Explain why the paper follows BERT, as many (Albert, LamDA, BLOOM, and so on) are counted as alternatives.
Ln. 119 missed something after ‘pipeline’, Check and revise it.
Ln. 129 Is the term "question-answer" abbreviated as "QA"? Please clarify whether or not this is the case. Do not use any abbreviations when first introducing the term.
Fıgure 2. Insert the abraviation as I, Ti into the Figure if possible.
Ln. 136 Please provide me with more information about stage 4. Can you please explain it in a better and clearer way?
Ln 165 Arrange the entities in alphabetical order in Figure 3.
Ln 182, insert “an arbitrary query text” in parenthesis after Tn.
Eq.1 & 2, The more general abbreviation of the cosine similarity is Sc. Thus, revise the equations.
Eq.1 & 2, After the summation in the denominator, should you have added the expression [k not equal to i]?
Ln 189 Replace the existing with “Similarly, an image-to-text loss for a single pair is calculated as”.
Eq 3., is the eq “the average loss across all N pairs” ? If so, please indicate this instead.
Ln 191, Please find below the revised text with corrections:
"Can you explain what hyperparameters are and why they are formed of Lambda and 1-lambda?" I believe this is because the equality of the two inputs is transposed. Could you explain this in more detail in the line?
Ln. 214. Jitter? Why does this exist?
Ln. 221 m ∈ [0, 1) is the momentum hyperparameter. (Parameters are the configuration model, which are internal to the model. Hyperparameters are the explicitly specified parameters that control the training process.)
Figure 5, Write the words in a right way (see the ‘steps’)
“The answer is detected, instead” can be written in Figure 5’s last step.
In conclusion, authors can discuss how to expand on their current work, methods, or evaluations by including a paragraph on future work. These future might work contain valuable research information and give the researchers hints of new research directions or ideas.
Author Response
Sure, here are the translations:
Comment 1: The question does not reflect the Figure. Please revise it as ‘Can an AI model learn from a literature collection and engage in question-and-answer format?’
Response: We have redrawn Figure 1 and rewritten the corresponding statement.
Comment 2: The statement 'Researchers can continue questioning, maintaining the coherence of their thoughts until they arrive at satisfactory scientific questions' does not align with the Figure. If the researchers are involved in ‘problem definition,’ there should be another arrow from the last step back to ‘the problem definition.’ Either the statement or Figure 1 should be revised to match better each other. This will not only improve the accuracy of the information presented but also enhance the overall quality of the work.
Response: We have redrawn Figure 1 and rewritten the corresponding statement.
Comment 3: Insert a new paragraph to guide the reader through the rest of the paper, highlighting the general organization of the following sections.
Response: We have rewritten the introduction according to your valuable suggestion.
Comment 4: Explain why the paper follows BERT, as many (Albert, LamDA, BLOOM, and so on) are counted as alternatives.
Response: We only need to extract text features from language encoders, and AlBERT (A Lite BERT) is optimized for NSP (Next Sentence Prediction, NSP), while LamDA (Language Models for Dialog Applications) is oriented towards dialogue tasks. These two contain redundant modules for our research objectives, and we need to consider their impact on the entire system in the experimental analysis section and conduct disintegration experiments for justification. Therefore, we did not consider them as language encoders for this study. BLOOM is an excellent open-source generative model project, but since our previous work used GPT (https://doi.org/10.3390/s24103130), we did not adopt BLOOM.
Comment 5: Ln. 119 missed something after ‘pipeline’, Check and revise it.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 6: Ln. 129 Is the term "question-answer" abbreviated as "QA"? Please clarify whether or not this is the case. Do not use any abbreviations when first introducing the term.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 7: Fıgure 2. Insert the abbreviation as I, Ti into the Figure if possible.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 8: Ln. 136 Please provide me with more information about stage 4. Can you please explain it in a better and clearer way?
Response: We have added a discussion on this. You can click the hyperlink in the attached document to go to the added paragraph.
Comment 9: Ln 165 Arrange the entities in alphabetical order in Figure 3.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 10: Ln 182, insert “an arbitrary query text” in parenthesis after Tn.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 11: Eq.1 & 2, The more general abbreviation of the cosine similarity is Sc. Thus, revise the equations.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 12: Eq.1 & 2, After the summation in the denominator, should you have added the expression [k not equal to i]?
Response: \( k \in \{1,2,…,N\} \), while \( i \in \{1,2,…,N\} \) is a specific value.
Comment 13: Ln 189 Replace the existing with “Similarly, an image-to-text loss for a single pair is calculated as”.
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 14: Eq 3., is the equation “the average loss across all N pairs”? If so, please indicate this instead.
Response: We have provided additional clarification. The N represents the batch size during training. You can click the hyperlink in the attached document to go to the modified section.
Comment 15: Ln 191, Please find below the revised text with corrections:
"Can you explain what hyperparameters are and why they are formed of Lambda and 1-lambda?" I believe this is because the equality of the two inputs is transposed. Could you explain this in more detail in the line?
Response: We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 16: Ln. 214. Jitter? Why does this exist?
Response: The term "Jitter" was inappropriate. We have made the necessary modifications. You can click the hyperlink in the attached document to go to the modified section.
Comment 17: Ln. 221 m ∈ [0, 1) is the momentum hyperparameter. (Parameters are the configuration model, which are internal to the model. Hyperparameters are the explicitly specified parameters that control the training process.)
Response: We have changed "parameter" to "Hyperparameter".
Comment 18: Figure 5, Write the words in a right way (see the ‘steps’)
“The answer is detected, instead” can be written in Figure 5’s last step.
Response: We have made the necessary modifications to Figure 5. You can click the hyperlink in the attached document to go to the modified section.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsDear Editor,
I am writing to you as a reviewer of the manuscript titled " Parameterization before Meta Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question Answering" which was submitted to Forests with the manuscript ID 2998802.
The article discusses the development of a novel approach to reorganizing literature content based on question-and-answer demands, to improve knowledge extraction and retrieval in meta-analysis. The authors discuss the potential applications of this approach in AI-assisted forestry ecology meta-analysis. I would rate this article as a 7 out of 10. The authors propose a novel approach to reorganizing literature content, which is innovative and potentially impactful. However, the article is written in a somewhat technical style, which may make it challenging for non-experts to understand. Overall, the article presents a promising approach to reorganizing literature content but could benefit from some revisions to improve clarity:
- Abstract:
Advice:
It is too technical and dense, making it difficult for readers to understand the main idea and significance of the research. The abstract jumps abruptly from discussing the need for deep learning technologies in forestry ecology to introducing a complex method for cross-modal representation extraction and embedding clustering. This makes it hard for readers to follow the author's argument.
Suggestion:
1. Simplify the language: Avoid using technical jargon and overly complex terms.
2. Focus on the main idea: Clearly state the main research question or problem being addressed in the paper.
3. Break up complex ideas: Instead of presenting a complex method for cross-modal representation extraction and embedding clustering, break it down into smaller, more manageable components.
4. Emphasize the significance: Highlight the importance of the research by explaining how it will contribute to the field of forestry ecology or address a specific problem.
- Introduction:
Advice:
This introduction is too dense and lacks a clear structure. The author jumps abruptly from discussing the limitations of current research methods to introducing a new concept, "parameterization before meta-analysis," without providing a clear connection between the two. The introduction also lacks a clear thesis statement, making it difficult for readers to understand the main argument.
Suggestion:
1. Start with a clear thesis statement: Begin with a concise statement that clearly outlines the main argument or purpose of the paper.
2. Provide context: Give readers some background information on the importance of forestry ecology and the limitations of current research methods.
3. Use transitions: Use transitions to connect ideas and paragraphs, making it easier for readers to follow the author's argument.
- Related Works:
Advice:
In this section, the author jumps abruptly from discussing the basics of language models to presenting various pre-trained models and techniques, without providing a clear connection between the ideas. Additionally, the text is heavy on technical jargon, making it difficult for readers to follow.
Suggestion:
1. Start with a clear overview: Begin with a brief summary of the main topic of the related work section, such as the importance of cross-modal representation learning in forestry ecology.
2. Use clear headings and subheadings: Break down the section into clear ones to help readers navigate the different ideas and concepts.
- Preliminary and Methods:
Advice:
Some sections provide too much detail, which can overwhelm the reader. For example:
- Cross-Modal Embedding Clustering: The section provides a detailed explanation of the contrastive learning approach and the design of the cross-modal momentum encoder. While this information is important for understanding the methodology, it may be too dense and technical for readers who are not familiar with these concepts.
Suggestion:
1. Providing a high-level overview of the methodology before diving into the details.
2. Identify the most important concepts in each section.
3. Enhance figure and table explanations. Provide a brief explanation of each figure and table's relevance to the text.
4. Provide clear explanations for how each section builds upon the previous one.
- Experiments and Results Analysis:
Advice:
1. The section "4.1. Settings" provides a brief overview of the experimental setup, but it lacks specific details about the datasets used, evaluation metrics, and hyperparameters.
2. The section "4.2. Main Results" presents several tables and figures, but the text lacks a clear explanation of the results and their implications.
3. The section "4.3. Ablation Study" presents an ablation study, but it lacks context for why this study was conducted and what it aims to achieve.
4. The section "4.4. Qualitative Analysis of Forestry Ecological Question-Answering" presents a qualitative analysis of the results, but it lacks a thorough discussion of the implications and limitations of the study.
Suggestion:
1. In the section "4.1. Settings": Provide more information about the experimental setup, including the specific datasets used, evaluation metrics, and hyperparameters.
2. In the section "4.2. Main Results": Provide a clear explanation of the results, highlighting the key findings and their significance.
3. In the section "4.3. Ablation Study": Provide context for the ablation study, including why it was conducted and what it aims to achieve.
4. In the section "4.4. Qualitative Analysis of Forestry Ecological Question-Answering": Provide a thorough discussion of the implications and limitations of the study, highlighting any potential biases or limitations.
Discussion:
Advice:
The discussion section lacks clarity, coherence, and specificity, making it difficult to understand the author's goals and plans for future research.
Suggestion:
1. The discussion section should provide a clear and concise overview of the study's goals and objectives, including what was achieved and what remains to be done.
2. The discussion section should provide specific details about the research directions, methods, and potential outcomes.
3. The discussion section should be organized logically and coherently, with clear transitions between ideas.
Conclusions:
Advice:
The Conclusions section lacks clarity, coherence, and specificity, making it difficult to understand the study's main findings and implications.
Suggestion:
1. The Conclusions section should provide a clear and concise summary of the study's main findings and implications.
2. The description of the study's contributions, limitations, and future research directions.
Ultimately, this article may be accepted after the requisite revisions and reassessment.
Thank you for considering my opinion.
Sincerely,
Author Response
Thank you very much for your valuable time and selfless assistance. These are the most detailed, specific, and helpful suggestions I have received. Your guidance on the paper is just like a professor guiding their own doctoral students. If possible, I would love to give you a big hug.
Due to the extensive and substantial nature of the revisions, we will not list each change individually here. The relevant modifications have been reflected in the attached document. Specifically, we have inserted a section explaining the revisions between the abstract and introduction in the manuscript. You can navigate to the corresponding modifications using the in-text hyperlinks in the attached document.
1. Abstract Section Revision
We have rewritten the abstract section.
2. Introduction Section Revision
We have rewritten the introduction section.
3. Related Works Section Revision
Based on your advice, we have added corresponding paragraphs to the manuscript.
4. Preliminary and Methods Section Revision
Based on your advice, we have added corresponding paragraphs to the manuscript.
5. Experiments and Results Analysis Section Revision
We have made modifications as per your suggestions, addressing each point individually.
6. Discussion Section Revision
We have thoroughly reviewed your suggestions and rewritten the discussion section after multiple readings of our manuscript.
7. Conclusion Section Revision
We have thoroughly reviewed your suggestions and rewritten the conclusion section after multiple readings of our manuscript.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsDear Authors,
You have presented a paper entitled Parameterization before Meta Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering, you have referred it to the review procedure in the journal Forest.
In my opinion, the manuscript was erroneously accepted for the procedure in this very journal, since the content in it predominantly deals with issues in computer science, modeling, machine learning. I realize that the main topic of the publication series "Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods" under which you want to publish this article is related to artificial intelligence, in my opinion your article contains too little content related to "forest".
Besides, a major shortcoming of the paper is the lack of a proper discussion of the results-comparison of the results of your own analysis with the results settled by other authors.
I suggest that the manuscript be withdrawn, refer in the content of the introduction and methodology to the forest and prepare a discussion of the results.
Author Response
Thank you very much for your valuable suggestions. With the strong support from the MDPI Forests editorial team and the assistance of multiple reviewers, we have made significant revisions to our manuscript. Specifically, we have rewritten the abstract, introduction, discussion, and conclusion sections, redrawn Figures 1 and 3, and provided more detailed discussions in the methods and experiments sections. The related modifications have been reflected in the attached manuscript. In particular, the newly written or added sections are marked in blue font, and the modified sections are marked in red font.
Author Response File: Author Response.pdf
Reviewer 5 Report
Comments and Suggestions for AuthorsOverall, the research builds interesting analyses, but the research needs to provide detailed implications to highlight the novelty of the research findings. Thus, several things need to be done and revised before being considered for publication.
The paper introduces an interesting and important analytical topic in the context of answering ecological questions using meta-analysis.
Firstly, I suggested using prospective objective research at the beginning of your abstract.
However, this abstract is still too general, so it needs to be reconstructed, some points are already there, it's just that general information need to be abstracted "more concisely". For example, the implications of this study should be emphasised.
In the introduction aspect, the first paragraph, lines 18-36, references are needed, especially in terms of scientific argumentation. Another example, line 51-52, from an information theory perspective, ... the weakness in this section is the literature study, to fill the novelty in the research gap, there is still a lack of sources of previous studies, therefore, restructuring would be better in this section.
Related works, line 61, the theoretical aspect of this research should make sense to challenge existing views or offer new insights into the topic. This should also not be lost at the end and/or appear in the discussion section. It might be helpful to explain how these ideas relate to any aims that can be developed from these discussions (lines 445-442).
As mentioned on page 3, line 112, "our primary contributions, compared to related works...."then empirical studies, specifically on AI model, meta-analysis, and forest ecology to be a clearer explanation, there needs to be practically taken from the literature. Also, citation needed.
In the Methods section, Figure 2, Step 2 regarding the literature dataset, the type of dataset should be clearly stated.
In line 148, "iNaturalist2017 dataset....", authors may provide the original link of the dataset. In the details, it may be better to version how the data collection could be in a table explaining the classification or type of data, data sources and time period.
Settings, in line 311, this section should be moved to the Method section.
In terms of results analysis of the paper is still vague, and more expecting to find an analysis dataset and its effectiveness that already proposed method. For example, refers to Table 2, 3, page 11, in the context of the method that have been used recently, where it should be mentioned some in the literature debates.
See also Figure 8, page 14, where the original question and figure should be included as a comparison, or this could be a limitation of the study.
Finally, the discussion section should begin with a reiteration of the purpose of the study to guide the reader as to what the paper is about. The discussion in the paper can be strengthened by considering the relevance of these findings to existing literature.
Author Response
Thank you very much for your valuable time and selfless assistance. We have incorporated the relevant modifications in the attached document. Specifically, we have inserted a section between the abstract and introduction in the manuscript to explain the revisions. You can navigate to the corresponding modifications by following the hyperlinks within the document. Please allow me to address your suggestions in the following aspects here.
Comment 1：Firstly, I suggested using prospective objective research at the beginning of your abstract.
Response：We have rewritten the abstract section.
Comment 2：However, this abstract is still too general, so it needs to be reconstructed, some points are already there, it's just that general information need to be abstracted "more concisely". For example, the implications of this study should be emphasised.
Response：Incorporating your first two suggestions as well as those from other reviewers, we have rewritten the abstract section.
Comment 3：In the introduction aspect, the first paragraph, lines 18-36, references are needed, especially in terms of scientific argumentation. Another example, line 51-52, from an information theory perspective, ... the weakness in this section is the literature study, to fill the novelty in the research gap, there is still a lack of sources of previous studies, therefore, restructuring would be better in this section.
Response：We have rewritten the introduction section.
Comment 4：Related works, line 61, the theoretical aspect of this research should make sense to challenge existing views or offer new insights into the topic. This should also not be lost at the end and/or appear in the discussion section. It might be helpful to explain how these ideas relate to any aims that can be developed from these discussions (lines 445-442).
Response：After carefully reviewing your suggestions, we have added relevant paragraphs to the Related Works section and also rewritten the Discussion section.
Comment 5：As mentioned on page 3, line 112, "our primary contributions, compared to related works...."then empirical studies, specifically on AI model, meta-analysis, and forest ecology to be a clearer explanation, there needs to be practically taken from the literature. Also, citation needed.
Response：After carefully considering your suggestions, we have supplemented the literature review on AI models, meta-analysis, and forest ecology, and included citations accordingly.
Comment 6：In the Methods section, Figure 2, Step 2 regarding the literature dataset, the type of dataset should be clearly stated.
Response：The literature dataset has been listed in the appendix, and citation links have been added to the relevant sections of the main text as mentioned by you.
Comment 7:In line 148, "iNaturalist2017 dataset....", authors may provide the original link of the dataset. In the details, it may be better to version how the data collection could be in a table explaining the classification or type of data, data sources and time period.
Response: Based on your suggestions, we have added supplementary notes in the specified sections 3.2 and included details such as the original link, classification or type of data, data sources, and time period of the iNaturalist2017 dataset in the appendix A.4.
Comment 8: Settings, in line 311, this section should be moved to the Method section.
Response: Based on your suggestion, we have moved "Settings, in line 311" to the Method section 3.9.
Comment 9:In terms of results analysis of the paper is still vague, and more expecting to find an analysis dataset and its effectiveness that already proposed method. For example, refers to Table 2, 3, page 11, in the context of the method that have been used recently, where it should be mentioned some in the literature debates.
Response: We have supplemented the discussion of the experimental results, such as those in Tables 2 and 3, with more detailed explanations and highlighted them in blue font4.1.
Comment 10: See also Figure 8, page 14, where the original question and figure should be included as a comparison, or this could be a limitation of the study.
Response: We have elaborated on the limitations of our proposed method in the section you mentioned.4.3.
Comment 11: Finally, the discussion section should begin with a reiteration of the purpose of the study to guide the reader as to what the paper is about. The discussion in the paper can be strengthened by considering the relevance of these findings to existing literature.
Response: After repeatedly reviewing your suggestions, we have rewritten the discussion section.5.
Author Response File: Author Response.pdf
Reviewer 6 Report
Comments and Suggestions for AuthorsParameterization before Meta Analysis: Cross-Modal
Embedding Clustering for Forest Ecology Question-Answering
1. The abstract does not align with the title of the paper. It should be revised to clearly outline the new contributions and provide a clearer explanation.
2. I do not think the authors followed MDPI guidelines to prepare the manuscript. They have to recheck Figures and Tables carefully. Figure 3 texts should be changed. The current format looks captured from somewhere.
3. Authors should mention the main contribution of the paper in the Introduction section.
4. Related Works. This section should survey recently published similar works to inform readers what authors doing in the same research environments. And authors can make (suggested) forest ecology protection subjection and mention following forest fire detection papers after detailed reading.
https://doi.org/10.3390/s23031512
https://doi.org/10.3390/pr12051039
5. What means Figure 2? Authors should give explanations to all figures in order to eliminate misunderstandings by readers or new researchers. Same as Tables.
6. The suggested method's shortcomings should have been mentioned by the authors, but they didn't.
Summary: This manuscript was written well but it should be clearly revised based on comments and readers should read many papers related to their research area to explain their ideas clearly and in detail. The manuscript is also missing a discussion on limitations and datasets, which should be included. Additionally, the language style in many sections is not suitable for a scientific paper. The presentation of results needs further refinement, as some results in tables and figures are not correctly displayed. Please address all these comments thoroughly and diligently.
Author Response
Thank you very much for your valuable time and selfless help. The relevant modifications have been reflected in the attached manuscript. Specifically, we inserted a section explaining the revisions between the abstract and the introduction. You can use the internal hyperlinks in the attached document to jump to the corresponding modifications. Please allow me to respond to your comments as follows:
Comment 1: The abstract does not align with the title of the paper. It should be revised to clearly outline the new contributions and provide a clearer explanation.
Response: After carefully considering your suggestion, we have rewritten the abstract.
Comment 2: I do not think the authors followed MDPI guidelines to prepare the manuscript. They have to recheck Figures and Tables carefully. Figure 3 texts should be changed. The current format looks captured from somewhere.
Response: After carefully considering your suggestion, we have reformatted Figure 3 into a table for a more organized presentation.
Comment 3: Authors should mention the main contribution of the paper in the Introduction section.
Response: We have clearly stated the main contributions of the paper in the Introduction section and further refined the discussion in this part.
Comment 4: Related Works. This section should survey recently published similar works to inform readers what authors doing in the same research environments. And authors can make (suggested) forest ecology protection subjection and mention following forest fire detection papers after detailed reading.
https://doi.org/10.3390/s23031512
https://doi.org/10.3390/pr12051039
Response: We have supplemented the Related Works section with additional discussions and added relevant paragraphs. Additionally, we found the two papers you mentioned very interesting and have cited them in our manuscript. You can view this in the attached document, where we provide hyperlinks to the locations where your referenced papers are cited.
Comment 5: What means Figure 2? Authors should give explanations to all figures in order to eliminate misunderstandings by readers or new researchers. Same as Tables.
Response: We have added corresponding paragraphs to provide a detailed explanation of the details of Figure 2.
Comment 6: The suggested method's shortcomings should have been mentioned by the authors, but they didn't.
Response: We have discussed the limitations of our proposed method in both the qualitative analysis experiment section and the discussion section.
Author Response File: Author Response.pdf
Round 2
Reviewer 4 Report
Comments and Suggestions for AuthorsDear Authors,
it is apparent that you have solidly rewritten the manuscript entitled Parameterization before Meta Analysis: Cross-Modal
Embedding Clustering for Forest Ecology Question-Answering. While I appreciate your input and commitment, you still have not complied with my suggestions regarding the discussion of the results. This section has been re-edited by you, but you have not compared the results of your own analysis with those of other authors. You have not cited existing publications in which the authors take the trouble to perform similar studies. Unfortunately, the manuscript in my opinion cannot be accepted in this form.
Author Response
Comment: it is apparent that you have solidly rewritten the manuscript entitled Parameterization before Meta Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering. While I appreciate your input and commitment, you still have not complied with my suggestions regarding the discussion of the results. This section has been re-edited by you, but you have not compared the results of your own analysis with those of other authors. You have not cited existing publications in which the authors take the trouble to perform similar studies. Unfortunately, the manuscript in my opinion cannot be accepted in this form.
Response: Thank you very much for your valuable suggestions. After carefully reviewing your advice, we have selected the experimental analysis sections of two cross-modal question-answering papers for comparison and added the relevant discussion to the "Qualitative Analysis of Forestry Ecological Question-Answering" section. The specific modifications are included in the attached document. Specifically, we have added a section between the abstract and the introduction, detailing our response to your valuable suggestions, and we have provided a hyperlink for you to access the specific revisions. Once again, thank you for your generous support and assistance with our work.
Author Response File: Author Response.pdf
Reviewer 5 Report
Comments and Suggestions for AuthorsThe authors have made significant revisions to this version, however still on the abstract seems to need to be reformulated "more concisely". There are some points that can be merged to avoid duplication, for example, lines 7-8, and 12-14, and even shorten the method section in the abstract from lines 9-20.
Author Response
Comment : The authors have made significant revisions to this version, however still on the abstract seems to need to be reformulated "more concisely". There are some points that can be merged to avoid duplication, for example, lines 7-8, and 12-14, and even shorten the method section in the abstract from lines 9-20.
Response: Thank you very much for your valuable suggestions. We have addressed the redundancy in lines 7-8 and 12-14 and made concise revisions throughout the abstract. Additionally, we have shortened and clarified the content in lines 9-20. Overall, we have streamlined the abstract for a more concise expression. Specifically, we have highlighted the revisions in the abstract section in red font. You can review the changes in the attached document.
Author Response File: Author Response.pdf
Reviewer 6 Report
Comments and Suggestions for AuthorsThe current version of the article has been revised, and meets the publication requirements.
Author Response
Comment : The current version of the article has been revised, and meets the publication requirements.
Response : Thank you very much for your valuable suggestions on our manuscript. Your recognition serves as a driving force for our research work. We deeply appreciate your selfless support and assistance once again.
