Influence of temperature on the behaviour and physiology of Atlantic salmon (Salmo Salar) on a commercial farm
Bibliographic record
Abstract
Commercial Atlantic salmon (Salmo salar) farms face challenges under climate change, as rising sea temperatures and higher variability in weather patterns can lead to thermal stress events, compromising fish health, and resulting in lower production efficiency. Temperature plays a critical role in influencing the physiological and behavioural response of salmon under stressful conditions and effective farm management requires a nuanced comprehension of its function to adjust farming practices and minimize further stress. Two types of biologgers, measuring external acceleration, depth, heart rate, and temperature were surgically implanted for 245 days to explore the effects of the thermal response of Atlantic salmon during a standard production cycle. Apparent heart rate scope and Arrhenius breakpoint temperature were used to estimate the optimal temperature and both methods resulted in an estimate of 12.7 °C. There was a reduction in apparent heart rate scope at temperatures below 2 °C and >19 °C suggesting proximity to limits of thermal stress. The use of biologgers facilitates direct observations in commercial operations, providing essential information for aquaculture management. These findings contribute to a holistic understanding of the effect of temperature influencing Atlantic salmon physiology and behaviour on aquaculture farms, bridging the gap between controlled laboratory studies and real-world commercial operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".