Bibliographic record
Abstract
This invited review is based upon a recent oral paper I presented at the Virtual Reality Symposium of the 34th International Ethological Conference (2015, Cairns, Australia), and as such it describes studies conducted mainly in my own laboratory. It reviews how we utilized visual stimuli for inducing behavioral responses in the zebrafish with a focus on shoaling, group forming behavior. The zebrafish is gaining increasing popularity in neuroscience. With this interest, its behavior is also more frequently studied. One of the many advantages of the zebrafish over traditional laboratory rodents is that this species is diurnal, and it relies heavily upon its visual system. Thus, similarly to our own species, zebrafish respond to visual stimuli in a robust and easily quantifiable manner. For the past decade, we have been exploring how to use such visual stimuli, and have developed numerous paradigms with which we can induce and quantify a variety of behavioral responses, including shoaling. This review summarizes some of these studies, and discusses questions including whether one should use live fish as stimulus, whether and how one could present animated (moving images) of fish, and how one could optimize a range of stimulus presentation parameters to elicit the most robust responses in zebrafish. Although the zebrafish is a relative newcomer in ethology and behavioral neuroscience, and although many of our findings only represent the first steps in this research, our results suggest that the behavioral analysis of the zebrafish will have an important place in biomedical research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".