Operationalizing climate risk in a global warming hotspot
Bibliographic record
Abstract
Abstract Climate change is a looming threat to marine life, creating an urgent need to develop climate-informed conservation strategies. The Climate Risk Index for Biodiversity was designed to assess the climate risk for marine species in a manner that supports decision-making. Yet, its regional application remains to be explored. Here, we use it to evaluate climate risk for ~2000 species in the northwest Atlantic Ocean, a marine warming hotspot, to explore its capacity to inform climate-considered fisheries management. Under high emissions, harvested species, especially those with the highest economic value, have a disproportionate risk of projected exposure to hazardous climate conditions but benefit the most from emission mitigation. By mapping critical risk areas for 90 fish stocks, we pinpoint locations likely to require additional intervention, such as in the southern Gulf of St. Lawrence for Atlantic cod. Finally, we demonstrate how evaluating climate risk geographically and understanding how it arises can support short- and long-term fisheries management and conservation objectives under climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".