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Record W2754740077 · doi:10.1073/pnas.1705957114

Trichromacy increases fruit intake rates of wild capuchins ( <i>Cebus capucinus imitator</i> )

2017· article· en· W2754740077 on OpenAlexafffund
Amanda Melin, Kenneth L. Chiou, Emily R. Walco, Mackenzie L. Bergstrom, Shoji Kawamura, Linda M. Fedigan

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersJapan Society for the Promotion of ScienceCanada Research ChairsMinistry of Education, Culture, Sports, Science and TechnologyUniversity of CalgaryLeakey FoundationWenner-Gren Foundation
KeywordsZoologyBiologyCommunicationGeographyPsychology

Abstract

fetched live from OpenAlex

), controlling for plant-level and monkey-level variables that may affect fruit intake rates. Over the course of 14 months, we collected behavioral data from 72 monkeys in Sector Santa Rosa, Costa Rica. We analyzed 19,043 fruit feeding events within 1,602 foraging bouts across 27 plant species. We find that plant species, color conspicuity category, and monkey age class significantly impact intake rates, while sex does not. When plant species and age are controlled for, we observe that trichromats have higher intake rates than dichromats for plant species with conspicuously colored fruits. This study provides clear evidence of trichromatic advantage in close-range fruit feeding in wild monkeys. Taken together with previous reports of dichromatic advantage for finding cryptic foods, our results illuminate an important aspect of balancing selection maintaining primate opsin polymorphism.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.388
Teacher spread0.299 · 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

Citations64
Published2017
Admission routes2
Has abstractyes

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