Behavioral Measure of Frequency Detection and Discrimination in the Zebrafish, <i>Danio rerio</i>
Bibliographic record
Abstract
Behavioral tests of hearing in fish are relatively rare and are generally based upon aversive conditioning, with little data available for the positive reinforcement methods common in other vertebrates. Despite its increasing importance as an auditory model, no behavioral hearing measures have been conducted on zebrafish (Danio rerio), with only physiological hearing estimates available. In the current study, a new behavioral testing paradigm is developed to assess sound detection abilities of zebrafish and the effect of training frequency on hearing sensitivity. Zebrafish were trained to respond to either a 400 Hz or a 1000 Hz tone, and behavioral thresholds were then measured to tones from 200 to 1000 Hz. Significant threshold differences existed between the behavioral audiograms, with fish from each set most sensitive to their conditioned frequency. Furthermore, fish acoustically conditioned to 1000 Hz were most sensitive to the upper range of test frequencies (600-1000 Hz). This appears to be the first study utilizing a positive reinforcement behavioral assay for testing hearing in zebrafish and provides further evidence of fine-scale auditory filtering in fish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".