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Record W2891278439 · doi:10.1126/science.aav4632

Vision using multiple distinct rod opsins in deep-sea fishes

2019· article· en· W2891278439 on OpenAlexfundno aff
Zuzana Musilová, Fabio Cortesi, Michael Matschiner, Wayne I. L. Davies, Jagdish Suresh Patel, Sara M. Stieb, Fanny de Busserolles, Martin Malmstrøm, Ole K. Tørresen, Celeste J. Brown, Jessica K. Mountford, Reinhold Hanel, Deborah L. Stenkamp, Kjetill S. Jakobsen, Karen L. Carleton, Sissel Jentoft, N. Justin Marshall, Walter Salzburger

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Eye InstituteOffice of Experimental Program to Stimulate Competitive ResearchAustralian Research CouncilOffice of Nuclear EnergyNorges ForskningsrådUniversitetet i OsloQueensland Brain InstituteKing Abdullah University of Science and TechnologyNorwegian Sequencing CentreNational Science CouncilEuropean Research CouncilGrantová Agentura České RepublikyNational Science FoundationUniversity of QueenslandNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of EnergyMcGill UniversityUniversität BaselWaitt Foundation
KeywordsOpsinPhotopigmentRhodopsinVertebrateBiologyEvolutionary biologyFish <Actinopterygii>AnatomyRetinaGeneGeneticsNeuroscienceRetinalFisheryBotany

Abstract

fetched live from OpenAlex

Fish catch color with rods Vertebrates are typically thought to have a consistent system for processing light, in which multiple cone opsins permit color vision during the day, but a single rod opsin provides only monochrome vision in the dark. Musilova et al. analyzed more than 100 deep-sea fish genomes and found a previously unknown proliferation of rod opsin genes that generate rod opsin photopigments that are tuned to different wavelengths of light. These receptors may allow the fish to perceive bioluminescent signals that pervade their deep-sea environment. These results reveal a previously undescribed visual system that allows for color vision in the dark. Science , this issue p. 588

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.055
GPT teacher head0.358
Teacher spread0.303 · 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

Citations258
Published2019
Admission routes1
Has abstractyes

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