Kings of the North: Bridging Disciplines to Understand the Effects of Changing Climate on Chinook Salmon in the Arctic–Yukon–Kuskokwim Region
Bibliographic record
Abstract
Abstract Understanding how species are responding to environmental change is a central challenge for stewards and managers of fish and wildlife who seek to maintain harvest opportunities for communities and Indigenous peoples. This is a particularly daunting but increasingly important task in remote, high-latitude regions where environmental conditions are changing rapidly and data collection is logistically difficult. The Arctic–Yukon–Kuskokwim (AYK) region encompasses the northern extent of the Chinook Salmon Oncorhynchus tshawytscha range where populations are experiencing rapid rates of environmental change across both freshwater and marine habitats due to global climate change. Climate–salmon interactions in the AYK region are a particularly pressing issue as many local communities have a deep reliance on a subsistence way of life. Here, we synthesize perspectives shared at a recent workshop on Chinook Salmon declines in the AYK region. The objectives were to discuss current understandings of climate–Chinook Salmon interactions, develop a set of outstanding questions, review available data and its limitations in addressing these questions, and describe the perspectives expressed by participants in this workshop from diverse backgrounds. We conclude by suggesting pathways forward to integrate different types of information and build relationships among communities, academic partners, and fishery management agencies.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".