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Record W3212703685 · doi:10.1111/mec.16279

Parallel shifts of visual sensitivity and body coloration in replicate populations of extremophile fish

2021· article· en· W3212703685 on OpenAlexafffund
Gregory L. Owens, Thor Veen, Dylan R. Moxley, Lenin Arias‐Rodríguez, Michael Tobler, Diana J. Rennison

Bibliographic record

VenueMolecular Ecology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British ColumbiaQuest University CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsBiologyHabitatExtremophileAdaptation (eye)EcologyOpsinPoeciliaNatural selectionEcological selectionLocal adaptationSelection (genetic algorithm)Evolutionary biologyZoologyFish <Actinopterygii>PopulationGeneticsBotanyFishery

Abstract

fetched live from OpenAlex

Abstract Visual sensitivity and body pigmentation are often shaped by both natural selection from the environment and sexual selection from mate choice. One way of quantifying the impact of the environment is by measuring how traits have changed after colonization of a novel habitat. To do this, we studied Poecilia mexicana populations that have repeatedly adapted to extreme sulphidic (H 2 S‐containing) environments. We measured visual sensitivity using opsin gene expression, as well as body pigmentation, for populations in four independent drainages. Both visual sensitivity and body pigmentation showed significant parallel shifts towards greater medium‐wavelength sensitivity and reflectance in sulphidic populations. Altogether we found that sulphidic habitats select for differences in visual sensitivity and pigmentation. Shifts between habitats may be due to both differences in the water's spectral properties and correlated ecological changes.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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