Differences in Acute Alcohol‐Induced Behavioral Responses Among Zebrafish Populations
Bibliographic record
Abstract
BACKGROUND: With the arsenal of genetic tools available for zebrafish, this species has been successfully used to investigate the genetic aspects of human diseases from developmental disorders to cancer. Interest in the behavior and brain function of zebrafish is also increasing as CNS disorders may be modeled and studied with this species. Alcoholism and alcohol abuse are among the most devastating and costliest diseases. However, the mechanisms of these diseases are not fully understood. Zebrafish has been proposed as a model organism to study such mechanisms. Characterization of alcohol's effects on zebrafish is a necessary step in this research. METHODS: Here, we compare the effects of acute alcohol (EtOH) administration on the behavior of zebrafish from 4 distinct laboratory-bred populations using automated as well as observation based behavioral quantification methods. RESULTS: Alcohol treatment resulted in significant dose-dependent behavioral changes but the dose-response trajectories differed among zebrafish populations. CONCLUSIONS: The results demonstrate for the first time a genetic component in alcohol responses in adult zebrafish and also show the feasibility of high throughput behavioral screening. We discuss the exploration and exploitation of the genetic differences found.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".