A case study on the effect of aquaculture operations on the physiology and behaviour of Atlantic salmon (Salmo salar) during two heat events on a commercial farm
Bibliographic record
Abstract
Aquaculture farms represent a complex 3D environment and face regular seasonal challenges such as acute and chronically elevated temperatures during summer. Further, fish are exposed to the interaction between their environment and farm operations, which can cause challenging conditions. In the context of modern net-pen aquaculture and ocean warming, there is therefore a need to understand the welfare of these commercially important species under the realistic conditions they encounter. Fish were tagged with two types of biologgers measuring temperature, heart rate, external acceleration, and depth of fish as they experienced standard aquaculture operations over two periods of thermal stress, one short-term and one long-term. The fish response during the thermal stress events was compared with the periods preceding and following both events, and an additional analysis was carried out to further explore the effects of feeding and farm operations. Fish displayed signs of both secondary and potentially tertiary stress in response to the short- and long-term heat event and both heart rate and acceleration increased in response to feeding but displayed a more nuanced response to operations. As part of the broader concept of precision fish farming, this research, based on data from 7 individual fish, represents a case study that presents the potential use of biologgers as tools for recognising early signs of stress by observing the secondary stress response, thereby demonstrating the potential for informed and timely stress identification to guide farm management decisions to enhance fish welfare and production efficiency in commercial aquaculture.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".