Comparing the behavioural thermoregulation response to heat stress by Atlantic salmon parr (<i>Salmo salar</i>) in two rivers
Bibliographic record
Abstract
Abstract Climate change is expected to increase the frequency and magnitude of extreme thermal events in rivers. The Little Southwest Miramichi River (LSWM) and the Ouelle River (OR) are two Atlantic salmon ( Salmo salar ) rivers located in eastern Canada, where in recent years, water temperatures have exceeded known thermal limits (~23°C). Once temperature surpasses this threshold, juvenile salmon exploit thermal heterogeneity to behaviourally thermoregulate, forming aggregations in coolwater refuges. This study aimed to determine whether the behavioural thermoregulation response is universal across rivers, arising from common thermal cues. We detailed the temperature and discharge patterns of two geographically distinct rivers from 2010 to 2012 and compared these with aggregation onset temperature. PIT telemetry and snorkelling were used to confirm the presence of aggregations. Mean daily maximum temperature in 2010 was significantly greater in the OR versus the LSWM ( p = 0.005), but not in other years ( p = 0.090–0.353). Aggregations occurred on 14 and 9 occasions in the OR and LSWM respectively. Temperature at onset of aggregation was significantly greater in the OR ( T onset = 28.3°C) than in the LSWM ( T onset = 27.3°C; p = 0.049). Logistic regression models varied by river and were able to predict the probability of aggregation based on the preceding number of hours >23°C ( R 2 = 0.61 & 0.65; P 50 = 27.4°C & 28.9°C; in the OR and LSWM respectively). These results imply the preceding local thermal regime may influence behaviour and indicate a degree of phenotypic plasticity, illustrating a need for localised management strategies.
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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 | 0.000 | 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) | 0.001 | 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".