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Record W2914924652 · doi:10.1126/science.aav3824

Linking a mutation to survival in wild mice

2019· article· en· W2914924652 on OpenAlexafffund
Rowan D. H. Barrett, Stefan Laurent, Ricardo Mallarino, Susanne P. Pfeifer, Charles C.Y. Xu, Matthieu Foll, Kazumasa Wakamatsu, Jonathan S. Duke‐Cohan, Jeffrey D. Jensen, Hopi E. Hoekstra

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaHoward Hughes Medical InstituteCanada Research ChairsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Geographic SocietyWorld Health Organization
KeywordsPeromyscusNatural selectionBiologyCoatSelection (genetic algorithm)AlleleGeneticsMutationEvolutionary biologyGeneZoologyEcology

Abstract

fetched live from OpenAlex

How natural selection affects mouse coat color Evolution, at its core, involves changes in the frequency of alleles subject to natural selection. But identifying the target of selection can be difficult. Barrett et al. investigated how allele frequencies affecting pigmentation change over time (see the Perspective by Pelletier). Wild-caught mice ( Peromyscus maniculatus ) were exposed to avian predators against naturally occurring dark or light backgrounds. Natural selection yielded shifts in coloration owing to genetic variants in the mouse coat color Agouti gene. Science , this issue p. 499 ; see also p. 452

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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

Citations210
Published2019
Admission routes2
Has abstractyes

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