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Record W6948729814 · doi:10.5061/dryad.547d7wm7p

The impact of methanol on behaviour: Dataset from zebrafish (Danio rerio) behavioural research

2021· dataset· en· W6948729814 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsMacEwan University
Fundersnot available
KeywordsZebrafishOpen fieldPreferenceFish <Actinopterygii>Field (mathematics)Test (biology)Methanol

Abstract

fetched live from OpenAlex

In many pharmacological and toxicological studies solvents are used as vehicles for the compound of interest. The solvents themselves may have effects on their own, therefore, it is important to test the impact of the solvent on the preparation prior to conducting the study. Methanol, (CH3OH), is a solvent used in many studies, and one we have used as a vehicle for benzo[a]pyrene. This data contains raw videos of zebrafish after exposure to 0, 0.25%, or 2.5% vol/vol, for 30 minutes prior to behavioral testing. Immediately after exposure, the individual zebrafish were moved to an open field test to quantify movement and location preference and were recorded for 10 minutes. Following the open field trial, a novel object was placed into the center of the arena to examine the exploratory response (boldness) of the fish for a second 10 minute trial. Motion-tracking software (EthoVision XT) was used to quantify the dependent variables (distance moved, time in outer 'thigmotaxis' zone, 'inner' zone, and 'transition' zone). There were no significant differences between groups for any of the variables in either the open field or novel object approach tests. This data suggests that 0.25% and 2.5% methanol does not alter locomotion or location preference in these two tests.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.110
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.128
GPT teacher head0.459
Teacher spread0.331 · 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.

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
Published2021
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicForensic and Genetic ResearchFrench-language works237,207