Zebrafish Are Able to Detect Ethanol in Their Environment
Bibliographic record
Abstract
Zebrafish have become a popular animal model for studying the development of alcohol addiction. Several behavioral paradigms for studying alcohol addiction have been developed for zebrafish, including conditioned place preference, alcohol-induced tolerance, and withdrawal. However, alcohol choice preference tasks have not been established in zebrafish as of yet. The ability of zebrafish to detect alcohol in their environment is required in alcohol choice or preference tasks. To our knowledge, it is currently unknown whether zebrafish are able to detect alcohol in their environment immediately following bath immersion. In the current study, we analyzed the time course of alcohol-induced behavioral changes of zebrafish while being immersed in alcohol solution in a 1.5 L tank. We recorded each trial in high-definition and quantified behavioral responses using automated video tracking-based and manual observation-based methods to quantify temporal changes in alcohol-induced behaviors. As alcohol is known to require several minutes of bath immersion to reach the brain in zebrafish, we argued that behavioral responses before this time point would prove zebrafish's ability to detect this substance in the water. Our results show that a 60-min exposure to 1% alcohol alters behavioral responses in a time-dependent manner. Notably, alcohol exposure significantly increased absolute turn angle, decreased distance to bottom, and variance of distance to bottom within the first 3 min immediately following exposure, a response that occurred before alcohol could reach the brain of the subjects in measurable amounts. These results imply that zebrafish are able to detect alcohol in their environment immediately following immersion into the drug solution.
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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".