Warm winters reduce landscape-scale variability in the duration of egg incubation for coho salmon (<i>Oncorhynchus kisutch</i>) on the Copper River Delta, Alaska
Bibliographic record
Abstract
We quantified the sum of daily mean temperature above 0 °C and modeled incubation duration using water temperature data collected at 12 coho salmon (Oncorhynchus kisutch) spawning sites during two incubation periods with cool, snow-dominant conditions and three incubation periods with anomalously warm, rain-transitional conditions, a proxy for a future climate scenario. Warmer water temperatures during warm–rain-transitional winters yielded a 58-day reduction in the median duration of egg incubation; however, the magnitude of change at individual sites varied widely and was controlled by water source. At groundwater-fed sites, temperature variations were strongly attenuated, leading to small interannual differences in incubation duration that were relatively insensitive to short-term changes in air temperature. In contrast, modeled incubation duration was shortened by up to 3 months during warm–rain-transitional winters at precipitation-fed sites. Remarkably, our modeling showed increased uniformity in incubation duration across the landscape during warm–rain-transitional winters. The potential loss of diversity in incubation duration during warmer winters, in isolation, may reduce portfolio effects in this region’s coho salmon population by promoting greater synchronization in the time of spawning.
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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 | 0.000 |
| 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) | 0.000 | 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".