Mapping suitable thermal migration corridors for western Alaska chum salmon in the North Pacific
Bibliographic record
Abstract
It is evident that warming North Pacific Ocean (NPO) temperatures are impacting salmon fitness and survival. Record-low western Alaska chum salmon run sizes were recorded in the Yukon River during 2020 and 2021. Based on recent analyses of Pacific salmon species-specific ocean temperature preferences, chum salmon have the widest thermal preference range; we focus on chum because they may have the greatest resilience to temperature variability and if their range is impacted, it is likely other species will respond to observed warming as well. Thermal suitability was mapped along the seasonal migration based on swimming rates to examine potential interannual range or distribution shifts. Two individual-based models of salmon migration driven by surface temperatures and geostrophic currents were run to test the impact of ocean physics on migration and distribution. We found that in the last decade ocean temperatures have similar magnitude and variability as projected through 2050. Since around 2013, the high suitability migration corridor has shifted northwards into the Bering Sea, but the seasonal migration may not be driven primarily by temperature or ocean currents. Warmer ocean temperatures, marine heatwaves, and loss of seasonal sea ice are likely to have the greatest impacts on western Alaska chum salmon where thermal suitability is lowest–in the Bering Sea in winter, and in the eastern Gulf of Alaska in summer. The impacts at the margins of their habitat range may lead to poor condition, a northward domain shift, and more fish entering the Arctic Ocean.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".