The timing of spawning migration: implications of environmental variation, life history, and sex
Bibliographic record
Abstract
During 1960–2002, the arrival times of all spawning male and female Atlantic salmon (Salmo salar L., 1758) and brown trout (Salmo trutta L., 1758) entering Dalälven River were recorded. To study the role of environmental variation in spawning migration timing, we used long-term temperature (river and sea) and river discharge data. For salmon, the spawning migration peak was strongly correlated with mean monthly sea and river temperatures during spring: salmon arrived earlier when temperatures were higher and later when temperatures were lower. River discharge explained little of the variation in migration timing. Female salmon migration showed a stronger correlation with temperature than male salmon migration, and female salmon arrived ≈18 days earlier than males. Trout showed a larger variation in their spawning migration, but river and sea temperatures and river discharge explained little of the variation. Trout females arrived ≈7 days earlier than males. The sea and river temperatures were highly correlated during the spawning migration, indicating that large climate processes determine the temperature regimes in the Baltic Sea and its tributaries. Time of arrival at the river was not correlated with ovulation date; a female salmon or brown trout arriving late could ovulate almost immediately, whereas a female arriving early could wait to ovulate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".