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Record W4233146585 · doi:10.3410/f.735732688.793560644

Faculty Opinions recommendation of Vision using multiple distinct rod opsins in deep-sea fishes.

2019· dataset· en· W4233146585 on OpenAlexfundno aff
Alapakkam P. Sampath

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2019
Typedataset
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
FundersOffice of Experimental Program to Stimulate Competitive ResearchNational Institute of General Medical SciencesOffice of Nuclear EnergyNorges ForskningsrådQueensland Brain InstituteKing Abdullah University of Science and TechnologyNorwegian Sequencing CentreNational Science CouncilGrantová Agentura České RepublikyUniversitetet i OsloNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of EnergyMcGill UniversityUniversität BaselWaitt FoundationNational Science Foundation
KeywordsOpsinPhotopigmentRhodopsinBiologyEvolutionary biologyRetinaNeuroscienceBotanyRetinal

Abstract

fetched live from OpenAlex

Vertebrate vision is accomplished through light-sensitive photopigments consisting of an opsin protein bound to a chromophore.In dim-light, vertebrates generally rely upon a single rod opsin (RH1) for obtaining visual information.By inspecting 101 fish genomes, we found that three

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.056
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0560.078

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 head0.086
GPT teacher head0.412
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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