An appetitive olfactory learning paradigm for zebrafish in their home tanks
Bibliographic record
Abstract
Olfaction is a subject of increasing interest in fish biology, but there are few learning paradigms available to investigate olfactory behaviour. In the present study, groups of zebrafish were trained in their home tanks retrofitted for automated conditioning with a microprocessor-controlled syringe pump and feeder to deliver odourant and food, respectively, and responses recorded remotely to minimize researcher interference. Fish were presented with phenylethyl alcohol (PEA), and a food reward was given 15 seconds later for experimental groups or at a variably delayed interval for controls. This schedule continued for 48 trials over four days. Both groups showed initial attraction to PEA, but by the end of Day 2 all fish exhibited reduced interest in the odourant. Further experiments with different fish indicated that this attraction was dependent upon high food motivation, as starved animals reacted more than non-starved animals to the novel stimulus. On Day 3, experimental fish began once again to show attraction associated with the odourant, thus indicating that they formed an association between the PEA and food reward, which increased by the end of Day 4. Conversely, control fish showed little or no response to the odourant on Days 3 and 4. When exposed to a water-only trial, trained fish largely ignored the cue, indicating that odour and not turbulence was the main stimulus for learning. This experiment demonstrated that an appetitive learning paradigm, using olfactory cues presented in home tanks, is both feasible and cost-effective for testing olfactory behaviour in zebrafish. • Aquatic olfactory conditioning often requires large water volume and dedicated tanks. • Learning is possible using standard 3 L tanks and reasonable water volumes. • Paradigm was automated via microprocessors to minimize experimenter influences. • Appetitive conditioning of adult zebrafish occurred over 25-30 trials spanning 3 days. • This method would be easily adapted to sub-adult zebrafish and other small teleosts.
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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) | 0.001 | 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 | 0.001 | 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".