Multiple environmental drivers across life stages influence Yukon River Chinook salmon productivity
Bibliographic record
Abstract
Recent declines of Yukon River Chinook salmon ( Oncorhynchus tshawytscha) populations have severely impacted people residing in the Yukon and Alaska. Evidence-based conservation strategies focused on stock recovery and adaptation are urgently needed but are limited by our understanding of what is driving declines, particularly in Canadian portions of the Yukon River basin. We examined how multiple environmental drivers may be influencing Canadian-origin Yukon River Chinook salmon productivity for eight populations over a 28-year period. We found that productivity was related to various environmental and ecosystem processes acting cumulatively over multiple life stages and across broad geographic scales. Productivity decreased in association with warmer upriver migration temperatures, wetter freshwater juvenile rearing habitats, and increasing abundances of potential marine competitors. In contrast, productivity increased in years with warmer and snowier winters and earlier spring onset. We found that different populations had similar relationships with environmental drivers, with potentially negative implications for regional fisheries stability. Our findings provide insight into how Chinook salmon are responding to rapid environmental change and can help inform salmon conservation initiatives and sustainable harvest strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".