Expanded, compressed, or equal? Interactions between spawning window and stream thermal regime generate three responses in modeled juvenile emergence for Pacific salmon
Bibliographic record
Abstract
Climate warming poses challenges to native fish, particularly at high latitudes. We used incubation models to explore how interactions between spawning timing and daily varying water temperature affected emergence timing for five species of Pacific salmon in 33 thermally diverse streams in south-central Alaska. Interactions between spawning timing and stream thermal regime led to three different emergence timing responses: (1) “expanded” by typically 2–3 times the duration of the spawning window for summer spawning salmon at streams with a large annual water temperature range; (2) “equal” in duration to the spawning window, regardless of spawning timing, at streams with upwelling groundwater; and (3) “compressed” for late-spawning salmon where water temperature was cooler at spawning than at emergence. Across all sites, a ±15-day range in spawning timing had influence similar to anomalously warm winters (+2 °C to +3 °C) on the emergence timing window. Differences among species, spawning timing, and thermal regimes suggest that a range of adaptations in spawning behavior will likely enable Pacific salmon populations to accommodate shifting thermal regimes during their early life history.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".