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Record W2963575715 · doi:10.21769/bioprotoc.3306

A Standardized Tank Design for the Light Dark Task in Zebrafish

2019· article· en· W2963575715 on OpenAlexafffund
Amanda Facciol, Steven Tran, Robert Gerlai

Bibliographic record

VenueBIO-PROTOCOL · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZebrafishWhite lightPreferenceTask (project management)Fish <Actinopterygii>Computer scienceArtificial intelligenceBiologyPhysicsEngineeringOpticsMathematicsFisheryStatistics

Abstract

fetched live from OpenAlex

The light dark paradigm is a common behavioral test used to screen a variety of pharmacological agents, including anxiogenics and anxiolytics. Although most often used in rodents, the light dark task has recently been adapted for use in zebrafish. However, a number of inconsistent findings have been reported for this species. Some have found zebrafish to prefer black, while others report a preference for light. Careful analysis of light dark preference experiments using zebrafish reveals significant variation in testing tank design and test conditions, including lighting and substrate color. Additionally, in some experiments the designated dark side of the testing tank is completely covered, producing a "cave-like" environment which further confounds results. Lastly, authors commonly use the terms "light vs. dark" interchangeably with "white vs. black", when these are two separate factors that may influence preference: illumination level vs. background shade. To address these limitations, we designed testing tanks that differentiate illumination vs. background shade preference in zebrafish. This design allows for simple standardization of light dark testing apparatus in zebrafish, and facilitates more reliable comparison across studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.697
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.339
Teacher spread0.319 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreProtocol

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

Citations7
Published2019
Admission routes2
Has abstractyes

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