PONE-D-25-15313Leveraging synthetic data produced from museum specimens to train adaptable species classification modelsPLOS ONE

Dear Dr. Blair,

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Gianniantonio Domina, Ph.D.

Academic Editor

PLOS ONE

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3. In your manuscript, please provide additional information regarding the specimens used in your study. Ensure that you have reported human remain specimen numbers and complete repository information, including museum name and geographic location.

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“This work was supported by a Mitacs Elevate grant to J.D.B, as well as contributions from the Canadian Museum of Nature Foundation. We thank the Canadian Museum of Nature for allowing access to their specimens, equipment, and facilities. We thank Greg Rand, Marie Helene Hubert, and Alan McDonald for their assistance with the specimen collections and scanning equipment. We also thank Drs Leonid Sigal, Michelle Tseng, and Rachel Germain for their insightful discussions.”

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Additional Editor Comments:

This MS deserves to be published on PLOS One. It is well written in a scientific form. The authors have to follow the suggestions of the reviewer to prepare your imporved MS. In addition do not have to forget to add the authors of species names the first time that them are cited.

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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

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

Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: Dear authors, I find the manuscript well written. It seems to me that the strategy you propose is solid and could be applied to several domains other than the example you provide.

I have one major concern only. In line 201, you state that you scaled the images to 224x224 pixels for both training and testing. It seems to me that this low resolution could bias the performance of the model, since it could lead to loss of details in the images. Can you please explain why you decided to operate at this resolution, and why? Have you compared other resolutions, in order to decide the one with the best cost/effectiveness ratio? Please, provide some metrics.

Best regards

SM

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Reviewer #1: Yes: Stefano Martellos

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Leveraging synthetic data produced from museum specimens to train adaptable species classification models

PONE-D-25-15313R1

Dear Dr. Blair,

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.

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Kind regards,

Gianniantonio Domina, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: