Incorporating local information to predict thermal stress for diverse species
Bibliographic record
Abstract
Pacific salmonids are incredibly diverse and critical for both ecosystems and human consumers. Although salmon conservation recognizes the importance of diversity for viability, most previous studies have oversimplified phenology and life history diversity that dictate local environmental exposure and influence responses to climate change. I combined subpopulation-level spatial distributions and phenologies with monthly stream temperature to explore modern and future patterns in freshwater thermal stress for 449 subpopulations across 21 coho salmon (Oncorhynchus kisutch) and steelhead trout (O. mykiss) management units, 14 of which are listed as either Threatened or Endangered under the United States Endangered Species Act. Under modern conditions, 37% of coho and 90% of steelhead subpopulations were exposed to thermally stressful conditions. Under a simple 2°C climate warming scenario, 91% of subpopulations and the majority of subpopulations in 20 of 21 management units would be thermally stressed during at least one life stage. For diverse species like salmon, incorporating local-scale phenology, spatial, and climatic information is imperative to identify subpopulations that will be negatively impacted by future climate warming without mitigating actions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".