A Novel Food Delivery Method for Learning Studies Detects Significant Differences in Food Preference in Zebrafish
Bibliographic record
Abstract
Despite decades of research with laboratory rodents, the mechanisms underlying learning and memory, and their impairment, are still not fully understood. The zebrafish is a newcomer in this research area, but it has shown great promise. Food is often employed as a reinforcer in learning tasks with rodents. However, for zebrafish, food has been a problematic reinforcer. Controlling timing and localization of its delivery is difficult. What food types zebrafish prefer is also rarely studied? Here, we describe a novel food delivery hardware and procedure. The apparatus is simple, cheap to manufacture, and easy to employ. Using this new method, we compare how zebrafish respond to three food types, artemia nauplii, crushed tropical fish flakes, and small zebrafish pellets. In binary choice tasks, we show that zebrafish spend significantly more time near the artemia delivery cylinder, swim closer to, and visit this cylinder more frequently compared to food cylinders delivering flakes or pellets, while responses to these latter two cylinders do not differ from each other. We conclude that the newly developed method allows the quantification of food preference in zebrafish, and that it will lead to the identification of highly rewarding food types for learning studies in this species.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".