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Record W4409356546 · doi:10.1126/sciadv.adv4828

Science-based suggestions to save the world’s rarest primate species <i>Nomascus hainanus</i>

2025· article· en· W4409356546 on OpenAlexaff
Xukai Zhong, Xia Huang, Changyue Zhu, Yuxin Wang, Colin A. Chapman, Paul A. Garber, Yuan Chen, Pengfei Fan

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsHabitatExtinction (optical mineralogy)BiologyPrimatePopulationInbreedingEcologyHabitat destructionZoologyEffective population sizeReproductionGenetic diversityDemographySociology

Abstract

fetched live from OpenAlex

) is the world's rarest primate species; however, insufficient data on its habitat suitability and genetic status impede evidence-based decisions for habitat restoration. Here, we conducted a comprehensive analysis of Hainan gibbons' energy intake and expenditure, reproductive parameters, and genetic diversity based on field research (March 2021 to December 2022) and long-term historical data (2003 to 2024). By comparing our results with those of captive gibbons and other free-feeding captive primates, we found that Hainan gibbons can obtain sufficient energy for growth and reproduction in their existing habitats. Furthermore, we identified an additional D-loop haplotype indicating that the current population is more genetically diverse than previously thought. However, recently formed adult male-female pairs are increasingly related, signaling a high risk for inbreeding within this small population. Based on these findings, we highlight an urgent need to expand available habitat by building corridors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.366
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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