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Rhodopsin population genetics and local adaptation: variable dim‐light vision in sand gobies is illuminated

2009· letter· en· W1787866469 on OpenAlexaff
Daniel Ebert, Rose L. Andrew

Bibliographic record

VenueMolecular Ecology · 2009
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyAdaptation (eye)RhodopsinIntraspecific competitionNatural selectionEcological geneticsEvolutionary biologyPopulationLocal adaptationSelection (genetic algorithm)PomatoschistusEcologyPopulation geneticsFish <Actinopterygii>BotanyFishery

Abstract

fetched live from OpenAlex

The visual pigments of fish are thought to be adapted to the variable spectral qualities of aquatic light environments. Most research on the role of natural selection on the evolution of rhodopsins and dim-light vision in fish has focused on variation among species and higher taxa. In this issue, Larmuseau et al. reveal substantial intraspecific sequence variation in RH1 (the rhodopsin gene) in sand gobies (Pomatoschistus minutus). Using population genetics and molecular evolution approaches, they detect positive selection on RH1 and find evidence for adaptation to local light conditions.

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)0.003
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 score0.007
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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
Published2009
Admission routes1
Has abstractyes

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