Cortisol predicts migration timing and success in both Atlantic salmon and sea trout kelts
Bibliographic record
Abstract
Kelts - individuals of anadromous fish species which have successfully spawned and may return to sea to repeat the cycle - are perhaps the least studied life stage of iteroparous fish species. To date, our understanding of what makes them successful in their return migration to sea is limited. We investigated the relationship between three physiological parameters (baseline cortisol, baseline glucose and low molecular weight antioxidants) and the timing and success of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) kelt migration. To do so, we combined blood samples obtained within 3 minutes of capture and acoustic telemetry to track 66 salmon and 72 sea trout as they migrated out of rivers, into fjords and out at sea. We show that baseline cortisol may be a good predictor of migration success. Individuals with high baseline cortisol levels exited the river earlier but were less likely to successfully reach the sea. Similar relationships were not observed with glucose or antioxidants. We provide the first evidence to support the role of physiological status in migration success in Atlantic salmon and sea trout kelts. Our findings contribute to our understanding of the relationship between physiology and fitness in wild animals. Further, we suggest that migration timing is a trade-off between stress and readiness to migrate.
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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".