Colored-Light Preference in Zebrafish ( <i>Danio rerio</i> )
Bibliographic record
Abstract
Over the past decade, the zebrafish has been increasingly employed in biomedical neuroscience research due to its numerous evolutionarily conserved features with mammals. Its simple brain and the several molecular tools available for this species make the zebrafish an appealing model to study mechanisms of complex brain functions, including learning and memory. Most learning paradigms developed for the zebrafish have employed visual stimuli as the associative cue. Spontaneous color preference is a potential confound in such studies. It has been analyzed in zebrafish using colored objects, but with conflicting results. It has rarely been explored with colored light, despite the increasing use of computer-generated visual stimuli. Here, we employ a light emitting diode (RGB-system) light-based color preference task in the plus-maze. In two independent experiments, zebrafish were tested in a four-choice or dual-choice condition by using four different-colored lights (red, green, blue and yellow). Our results suggest a light preference hierarchy that depends on context, since yellow was preferred over green in the four-choice condition whereas blue was preferred over all other colors in the two-choice condition. These results are useful for future color-light-based learning experiments in zebrafish.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".