Do laboratory rearing conditions affect auditory and mechanosensory development of zebrafish (<i>Danio rerio</i>)?
Bibliographic record
Abstract
The effect of anthropogenic noise on the fish auditory system has become of increasing concern due to possible detrimental effects of intense sounds on auditory function and structures. This is especially problematic when raising fish in laboratory and aquaculture settings using filtration and aeration, which increase sound levels. To assess the effects of laboratory rearing conditions, one group of zebrafish (Danio rerio) embryos (‘‘controls’’) were placed into aerated aquaria in a normal laboratory rearing environment. A second set of embryos (‘‘quiet’’) were reared in aquaria with no aeration or filtration in a sound-resistant room. The intensity difference between the two sets of tanks was over 30 dB. Preliminary data show that there was no affect of differential rearing environments on saccular hair cell numbers or on hearing ability in fish up to 25 mm total length. However, rearing environment did affect neuromast number. ‘‘Quiet’’ fish had higher numbers of both cephalic and trunk superficial neuromasts, relative to controls. This difference was maintained up to 11 mm total length (the size at which canal formation begins). This suggests that acoustic environments normally found in the laboratory do not affect development of hearing in zebrafish, although laboratory acoustics may affect mechanosensory development.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".