Migration water temperature and heat stress assessments in western Alaska Chinook salmon overlapping the 2019 heatwave
Bibliographic record
Abstract
Chinook salmon population declines span their geographic range with climate hypothesized as a major driver. Concerns of warming freshwater temperatures in their northern range gained urgency during 2019 when a heatwave coincided with premature mortality. This study examined heat stress during the 2019 heatwave compared to subsequent years and described water temperatures in western Alaska to understand the degree to which freshwater temperatures may be a stressor. Heat stress was prevalent among Chinook salmon captured in the 2019 heatwave (Kuskokwim tributaries: 90% in Kwethluk and 63% Takotna river), and variable in subsequent years (∼8% to 60% across Kuskokwim tributaries and Norton Sound rivers). A review of water temperature data indicated that potentially stressful temperatures (≥18 °C) were most common and prolonged in the Yukon River, moderately common and prolonged in the Kuskokwim River, and relatively rare in the Norton Sound region. Water temperatures in 2019 broke several records for overall maximum and frequency of temperatures ≥ 18 °C. Migration water temperatures and heat stress in northern Pacific salmon habitats vary more widely than previously recognized (up to 25 °C).
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".