PONE-D-24-20285Fishing ground estimation through weakly supervised keypoint detection and meta-learningPLOS ONE

Dear Dr. Iiyama,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 04 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Lei Chu

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse.

3. Thank you for stating the following financial disclosure:

“Japan Society for Promotion of Science,  KAKENHI, Grant Number 21H04913.

Japan Science and Technology Agency, CREST, Grant Number JPMJCR19F1.”

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

4. We note that you have indicated that there are restrictions to data sharing for this study. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

Before we proceed with your manuscript, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., a Research Ethics Committee or Institutional Review Board, etc.). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see

https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible.

We will update your Data Availability statement on your behalf to reflect the information you provide.

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

After careful evaluation, we find the manuscript promising but believe that minor revisions are necessary to improve clarity and detail. Specifically, we recommend restructuring the introduction into distinct paragraphs to better emphasize the research background, limitations of existing methods, and contributions of the paper. Additionally, please provide more concise descriptions of keypoint detection in the method section, expand on the data preprocessing and annotation processes, and add detailed information about the experimental design, including dataset division, hyperparameters, and evaluation metrics.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study introduces a method to estimate fishing grounds using weakly supervised keypoint detection and meta-learning. Traditional methods, such as habitat suitability index models and statistical approaches, often overlook broader environmental patterns crucial for fishers. This research focuses on leveraging sea surface temperature (SST) patterns to predict fishing ground locations, viewing the fisher's decision-making process as a pattern recognition task. However, there are areas for improvement regarding the readability of the paper and the scientific contribution as follows:

1. The introduction contains a lot of information but lacks a clear paragraph structure. Please divide the introduction into several paragraphs, each focusing on a specific topic, such as research background, limitations of existing methods, and contributions of this paper.

2. When describing the method, there is a lack of concise description of the specific details of keypoint detection.

3. The description of the data is not detailed enough, especially the process of data preprocessing and annotation generation.

4. The description of the experimental design and the division of the data set is not detailed enough. Please add detailed description of the experimental design, including the division of the data set (training set, validation set and test set), the selection of hyperparameters, evaluation indicators, etc.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-24-20285R1Fishing ground estimation through weakly supervised keypoint detection and meta-learningPLOS ONE

Dear Dr. Iiyama,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Key challenges include the reliance on fishing data, which limits generalizability, and low F1-scores compared to traditional tasks, highlighting the need for deeper analysis and stricter evaluation metrics. The use of noisy trajectory data for weak supervision requires stronger validation, while the innovative application of meta-learning lacks comparisons to simpler alternatives like data augmentation. Expanding beyond skipjack tuna to multiple species and including comparisons with state-of-the-art methods in weakly supervised learning could enhance the study's relevance and impact. Furthermore, exploring alternative model architectures, improving the clarity of visualizations, and re-evaluating the suitability of hinge loss would provide deeper insights and strengthen the findings. Integrating environmental data (e.g., sea currents) could also enrich the dataset and improve accuracy.

Please submit your revised manuscript by Feb 13 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Lei Chu

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The paper titled "Fishing ground estimation through weakly supervised keypoint detection and meta-learning" explores innovative methods for estimating fishing grounds, which are crucial for optimizing fishing activities. The authors, Masaaki Iiyama and Kazuki Takasan, focus on the decision-making processes of fishers, particularly how they utilize sea surface temperature (SST) patterns to identify potential fishing areas. The study recognizes the limitations of traditional empirical methods that rely on environmental data, which often yield inconsistent results due to the complexities of marine environments and variations in fisher expertise. To address the challenge of insufficient annotated data for training predictive models, the authors propose a novel framework that combines weakly supervised pre-training with meta-learning. They leverage publicly available trajectory data from fishing vessels as a form of weak supervision. While this trajectory data does not always provide precise locations of fishing grounds, it offers valuable insights into broader patterns of fishing activity. During the pre-training phase, the model learns these patterns from trajectory data, which enhances its ability to recognize SST characteristics relevant to fishing.

The authors emphasize the importance of fine-tuning the model with more accurate catch data after the initial pre-training. This dual-phase approach aims to improve prediction accuracy despite the scarcity of reliable catch data. Additionally, to counteract the impact of noisy labels present in trajectory data—such as locations where vessels did not actually fish—the authors introduce a meta-learner. This component evaluates label reliability and minimizes the influence of misleading data on the model's learning process. The experimental results presented in the paper demonstrate that their proposed methods significantly enhance the estimation of fishing grounds compared to traditional approaches. The findings underscore the effectiveness of integrating weakly supervised learning and meta-learning techniques in addressing data limitations in marine resource management. Overall, this research contributes valuable insights into utilizing advanced machine learning methodologies for practical applications in the fishing industry, ultimately aiming to improve efficiency and sustainability in fishing practices.

Authors might consider the following comments:

• The title emphasizes the methodology ("weakly supervised keypoint detection and meta-learning") but does not highlight the outcome or potential implications (e.g., enhanced fishing efficiency, improved ecological management). Therefore, I would suggest to shift the focus toward the practical impact of the research. For example: "Improving Fishing Ground Predictions with Weak Supervision and Meta-Learning".

• While the abstract mentions "effectiveness of pre-training and meta-learning," it does not provide quantitative outcomes or a concrete comparison to existing methods. Including key performance metrics (e.g., improvement in F1-score) would better highlight the study's contributions. Additionally, terms like "weakly supervised pre-training" and "meta-learning" may be unclear to readers unfamiliar with machine learning. A brief simplification or explanation of these terms would improve accessibility.

• In my opinion, while the introduction highlights the scarcity of annotated data as a motivation for weakly supervised learning, it lacks a broader context about how this approach compares to or complements other learning paradigms in similar domains (e.g., transfer learning, semi-supervised learning). Also, the focus on pattern recognition in sea surface temperature (SST) data could be enriched by drawing analogies to neural mechanisms involved in processing spatiotemporal patterns, such as those in the visual cortex. In this regards, authors might consider further discussing how the decision-making process in fishers parallels neural mechanisms in humans, such as how the prefrontal cortex integrates environmental cues (e.g., SST patterns) to make predictions and decisions [https://doi.org/10.3390/ijms25052724; https://doi.org/10.1016/j.brat.2024.104548; https://doi.org/10.1111/nyas.15145].

• The reliance on fishing vessel trajectory and catch data limits the scope of generalizability. Additional sources, such as environmental factors (e.g., sea current velocity, chlorophyll levels), could enrich the dataset and enhance model accuracy.

• The F1-scores for the proposed method are significantly low, especially compared to traditional keypoint detection tasks. This discrepancy needs a deeper discussion of potential causes and strategies for improvement beyond the suggestions in the limitations section.

• The geodesic distance threshold (200 km) for evaluating accuracy might be overly lenient. A stricter metric could provide better insights into the method's precision and applicability.

• The paper highlights noisy and imprecise trajectory data influencing pre-training. The justification for using trajectory data as weak supervision requires further validation to strengthen the rationale for this approach.

• The use of meta-learning to mitigate noisy labels is innovative, but the paper lacks quantitative comparisons against simpler alternatives (e.g., data augmentation or filtering). Demonstrating the necessity and added value of meta-learning in this context would solidify the contribution.

• While the authors compare their method to alternatives like "catch only" and "fine-tuning," the addition of comparisons to state-of-the-art methods for weakly supervised learning in similar domains could enhance the impact of the results.

• The choice of Lightweight OpenPose for keypoint detection is reasonable, but an ablation study exploring the impact of alternative architectures (e.g., High-Resolution Networks or DeepLab) could provide more depth to the findings.

• The experimental focus on skipjack tuna limits the study's broader applicability. Expanding the analysis to include multiple species or regions could make the method more universally relevant.

• Figures showing heatmaps and output predictions (e.g., Fig. 11) could benefit from enhanced clarity and consistent formatting. Overlapping visualizations could obscure key findings, so better differentiation between estimated and ground-truth points would help.

• The results using hinge loss indicate decreased precision in some cases. This requires a more thorough examination and explanation of why hinge loss might not align with the task's objectives.

Reviewer #3: A technically sound study and displayed a rigour and depth perspectives of application of OpenPose & its experimental design

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Improving Fishing Ground Estimation with Weak Supervision and Meta-Learning

PONE-D-24-20285R2

Dear Dr. Iiyama,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Lei Chu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: