Zebrafish aversion to infrasound in an open field test
Bibliographic record
Abstract
Aquatic species are capable of detecting infrasound (sub-20 Hz frequencies) which may be a source of anthropogenic pollution and have a detrimental impact on the environmental fitness of fish. Infrasound is generated by infrastructure, producing acoustic frequency peaks that are not discernible by humans. The presence of these frequencies may therefore impact the environmental wellbeing of aquatic laboratory animals, which are often housed in spaces adjacent to facilities producing infrasound. To investigate the potential impact of infrasound, we used wild-type zebrafish (Danio rerio) and exposed them to short periods of infrasound at either 5, 10, 15, or 20 Hz, or 0 Hz as a control group. A motion-tracking software system was used to monitor fish movement in an open field test and arena location, distance moved, and immobility were quantified. There was a significant effect of 15 Hz which caused the fish to spend more time away from the infrasound source. The 20 Hz group also spent significantly less time in the zone closest to the speaker. There were no differences in distance moved or immobility between infrasound and control groups. These findings demonstrate that 15 Hz infrasound has aversive effects on zebrafish, causing them to move away from the sound source. To enhance environmental enrichment and wellbeing of aquatic laboratory animals, sources of infrasound pollution should be investigated and mitigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".