Potential climate change impacts on thermal habitats of Pacific salmon (<i>Oncorhynchus</i> spp.) in the North Pacific Ocean and adjacent seas
Bibliographic record
Abstract
We developed spatially explicit representations for seasonal high-seas (open ocean) thermal habitats for six species of Pacific salmon ( Oncorhynchus spp.) and evaluated the effects of natural climate variability and projected changes under three Intergovernmental Panel on Climate Change scenarios of future greenhouse gas emissions. Changes in high-seas habitat due to natural climatic variation in 20th century were small relative to that under anthropogenic climate change scenarios for the middle to late 21st century. Under a multimodel ensemble average of global climate model outputs using A1B (medium) emissions scenario for the entire study area (North Pacific and part of Arctic Ocean), projected winter habitats of sockeye ( Oncorhynchus nerka ) decreased by 38%, and summer habitat decreased by 86% for Chinook ( Oncorhynchus tshawytscha ), 45% for sockeye, 36% for steelhead ( Oncorhynchus mykiss ), 30% for coho ( Oncorhynchus kisutch ), 30% for pink ( Oncorhynchus gorbuscha ), and 29% for chum ( Oncorhynchus keta ) salmon by 2100. Reductions were 25% lower for B1 (lower) emissions and 7% higher for A2 (higher) emissions scenarios. Projected habitat losses were largest in the Gulf of Alaska and western and central subarctic North Pacific. Nearly complete losses of Gulf of Alaska habitat for sockeye in both seasons and Chinook in summer raise important policy issues for North American fishery managers and governments.
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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) | 0.001 | 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".