Long‐Term Behavioral Changes in Response to Early Developmental Exposure to Ethanol in Zebrafish
Bibliographic record
Abstract
BACKGROUND: Zebrafish is becoming an important research tool for the analysis of brain function and behavior. It has been proposed to model human alcoholism as well as fetal alcohol syndrome. Previous studies investigating the consequences of exposure to ethanol during early development of zebrafish employed robust dosing regimens (high ethanol concentration and long exposure) that may model a rare situation in the human clinic. These studies found major structural abnormalities developing in the exposed fish. METHODS: Here we hope to avoid such gross changes and administer only low doses of ethanol (0.00, 0.25, 0.50, 0.75, 1.00 vol/vol %) at 24-hour postfertilization and for only a short period of time (for 2 hours). We analyze the behavior of exposed fish at adult stage using computerized stimulus presentation and automated videotracking response quantification. RESULTS: Despite the short ethanol exposure period and the modest concentrations, significant behavioral alterations were found: fish exposed to higher doses of ethanol swam at an increased distance from a computer-animated zebrafish shoal while their activity levels did not change. CONCLUSIONS: Although the interpretation of and the mechanisms underlying this finding will require further investigation, the results suggest that zebrafish will be an appropriate model organism for the analysis of the effects of moderate to mild prenatal ethanol exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".