No trout about it: behavioural and transcriptional effects of long-term noise exposure in brook trout (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Exposure to acute noise sources can lead to negative behavioural outcomes and fitness deficits in fishes, but it is unknown whether fish can habituate to chronic exposures. As underwater noise increases globally, understanding how long-term exposures can affect behavioural, morphological, and transcriptional measures of stress in fish is critical. We tested responses in captive brook trout ( Salvelinus fontinalis) immediately after acute exposure to a noise and after 2 weeks of chronic exposure. Behavioural tests quantified fish movements before, during, and after sound presentation, with morphological and transcriptional changes assessed through ears and whole brains samples, respectively. Pre-control and pre-experimental brook trout exhibited increased swimming distance and velocity, but after the 2-week exposure, post-experimental fish showed no response to noise while the post-control group remained responsive. Post-experimental fish showed significant differences in transcription levels of genes involved in neuroplastic, appetite, and stress responses relative to the other groups. Together these results suggest that while fish may appear unresponsive via behavioural metrics to anthropogenic noises after chronic exposure, they still show significant changes at the transcriptional level with possible long-term effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".