MétaCan
Menu
Back to cohort
Record W2289134688 · doi:10.1098/rspb.2016.0058

Diminished foraging performance of a mutant zebrafish with reduced population of ultraviolet cones

2016· article· en· W2289134688 on OpenAlexafffund
Iñigo Novales Flamarique

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForagingZebrafishBiologyPopulationPredationFish <Actinopterygii>ZoologyEcologyFisheryGenetics

Abstract

fetched live from OpenAlex

Ultraviolet (UV) cones are photoreceptors that sense light in the range 300-450 nm and are found in the retinas of non-mammalian vertebrates and small mammals. Despite their widespread presence across taxa, the functions that these cones exert in the lives of animals remain largely unknown. In this study, I used the zebrafish lor (lots of rods) mutant, characterized by a diminished UV cone population compared to that of wild-type zebrafish, to test whether its foraging performance differed from that of the wild-type (control). The mean location distance and angle (variables that are reliable indicators of foraging performance) at which control fish detected zooplankton prey were, on average, 24 and 90% greater than corresponding measures for lor fish. Such inferior foraging performance of the mutant could be explained by reduced contrast perception of the prey, resulting from the diminished population of UV cones and associated sensitivity. Thus, UV cones enhance the foraging performance of zebrafish, a crucial ecological function that may explain why small zooplanktivorous fishes retain UV cones throughout their lives.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.208
Teacher spread0.195 · 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 designBench or experimental
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

Citations62
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Royal Society B Biological SciencesSame topicFish Ecology and Management StudiesFrench-language works237,207