Peer Review History

Original SubmissionOctober 14, 2021
Decision Letter - Siuly Siuly, Editor

PONE-D-21-33028Using Machine Learning to Understand Age and Gender Classification Based on Infant TemperamentPLOS ONE

Dear Dr. Gartstein,

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.

The idea of the paper is good and has potential to create new knowledge in that research area.Authors should provide more clearer explanation about  the motivation of the study, research problem and contribution of this research.Two reviewers provided their comments to improve the quality of the paper.

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We look forward to receiving your revised manuscript.

Kind regards,

Siuly Siuly, PhD

Academic Editor

PLOS ONE

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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

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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: No

Reviewer #2: Yes

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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: Yes

Reviewer #2: Yes

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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: By applying machine learning techniques, this paper analyzed the temperament features of infants and classified these features into gender and age categories. It is a significant improvement of traditional analytical investigation and a very meaningful research area for young children under 12 months, yet a few comments.

(1) In the section "Analytic Strategy" line 254, it mentioned the "gender by age group analyses". However, there is no Table or Figure in the paper to further elucidate these model types. Please try to provide supplemental information or move this group to the "Discussion" section as future work.

(2) For Table 3a, "Gender and age-based classification with temperament features", three metrics, i.g. Accuracy, Kappa, AUC, are applied to evaluate each of 11 machine learning algorithms. I hesitate to ask, what features do you use to do Gender Classification and Age Classification? I thought the features were those 14 models listed in Table 2, line 294. If not, please further explain. If yes, how to integrate 14 features' results into one metric?

Reviewer #2: This manuscript represents a meta-analysis utilizing IBQ-R data collected across multiple laboratories to overcome the limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. This paper is generally well written, logical and discusses a hot topic. The results also present a good effectiveness. I think this paper can be accepted.

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Reviewer #1: No

Reviewer #2: No

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Revision 1

November 14, 2021

Dr. Siuly,

Academic Editor

PLOS ONE

Dear Dr. Siuly,

Thank you for the opportunity to revise and resubmit our manuscript, entitled: “Using Machine Learning to Understand Age and Gender Classification Based on Infant Temperament.” We appreciate your appraisal of the idea behind our paper as good, with potential to create new knowledge in the research area. In this revision, we provide a clearer explanation regarding the motivation behind the study, the research problem addressed, and the contribution of this research in the Introduction and Discussion sections (pgs. 10, 22, 26, 27). With respect to data sharing, there are ethical/legal restrictions on sharing the de-identified data set used in this study imposed by contributors’ institutions (e.g., Institutional Review Board, Human Research Protection Program, Office of Research). In fact, the first author and her institution had to enter into Memorandum of Understanding agreements with a number of contributing sites in order to obtain the relevant data. These types of arrangements can be considered upon request to access data to the first and corresponding author: Maria A. Gartstein (gartstma@wsu.edu). I would also like to note that the ethical guidelines of the American Psychological Association were closely followed in conducting research presented herein. We have included the full names of the ethics committees that approved data collection across the 29 sites, indicating that all co-authors/investigators obtained written informed consent.

Reviewers’ comments and recommendations are addressed in detail below.

Reviewer #1: By applying machine learning techniques, this paper analyzed the temperament features of infants and classified these features into gender and age categories. It is a significant improvement of traditional analytical investigation and a very meaningful research area for young children under 12 months, yet a few comments.

(1) In the section "Analytic Strategy" line 254, it mentioned the "gender by age group analyses". However, there is no Table or Figure in the paper to further elucidate these model types. Please try to provide supplemental information or move this group to the "Discussion" section as future work.

####################################################################

We apologize if this element of the results was confusing, as Table 3b in fact presents gender by age group findings, one component of which (AUC indicators across considered algorithmic models) is also illustrated in Figure 1a-1c. We have clarified the latter for the reader in the revision, as requested (pgs. 19-20). ####################################################################

(2) For Table 3a, "Gender and age-based classification with temperament features", three metrics, e.g. Accuracy, Kappa, AUC, are applied to evaluate each of 11 machine learning algorithms. I hesitate to ask, what features do you use to do Gender Classification and Age Classification? I thought the features were those 14 models listed in Table 2, line 294. If not, please further explain. If yes, how to integrate 14 features' results into one metric?

####################################################################

The reviewer is correct insofar as the features are the 14 temperament scales, namely: Activity Level, Smiling/Laughter, Approach, High Intensity Pleasure, Perceptual Sensitivity, Vocal Reactivity, Fear, Distress to Limitations, Sadness, Falling Reactivity, Duration of Orienting, Soothability, Cuddliness/Affiliation, and Low Intensity Pleasure. On the other hand, Accuracy, Kappa, AUC, are indicators used to evaluate the predictive accuracy of the 11 machine learning algorithms considered in this study that rely on the 14 temperament features for gender, age, and gender by age classifications. In the revision, we have clarified related language (pgs. 10, 16, 22), increasing clarity according to this recommendation. ####################################################################

Reviewer #2: This manuscript represents a meta-analysis utilizing IBQ-R data collected across multiple laboratories to overcome the limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. This paper is generally well written, logical and discusses a hot topic. The results also present a good effectiveness. I think this paper can be accepted.

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We thank the reviewer for this positive view of our manuscript.

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We hope that you and the reviewers find this manuscript worthy of publication in PLOS ONE.

Attachments
Attachment
Submitted filename: PONE-D-21-33028R1_RespLet_FIN.docx
Decision Letter - Siuly Siuly, Editor

Using Machine Learning to Understand Age and Gender Classification Based on Infant Temperament

PONE-D-21-33028R1

Dear Dr. Gartstein,

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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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,

Siuly Siuly, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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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 #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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 #1: Yes

Reviewer #2: 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 #1: Yes

Reviewer #2: 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 #1: In this revised version, major changes are made in Introduction, Materials and Methods, and Discussion.

(a) In the Introduction session, the disadvantage of existing classification method is elaborated.

(b) In the Materials and Methods session, detailed researcher list is given.

(c) In the Discussion session, contributions of this paper is emphasized and the ensembling modeling approach is mentioned as the potential improvement in further work.

Other typos are clear in this version. Good job.

Reviewer #2: This manuscript represents a meta-analysis utilizing IBQ-R data collected across multiple laboratories to overcome the limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. This paper is generally well written, logical and discusses a hot topic. The results also present a good effectiveness. I think this paper can be accepted.

**********

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 #1: No

Reviewer #2: No

Formally Accepted
Acceptance Letter - Siuly Siuly, Editor

PONE-D-21-33028R1

Using Machine Learning to Understand Age and Gender Classification Based on Infant Temperament

Dear Dr. Gartstein:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Siuly Siuly

Academic Editor

PLOS ONE

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