Operationalizing climate risk in a global warming hotspot
Bibliographic record
Abstract
Abstract There has been a proliferation of climate change vulnerability assessments of species, yet possibly due to their limited reproducibility, scalability, and interpretability, their operational use in applied decision-making remains paradoxically low. We use a newly developed Climate Risk Index for Biodiversity to evaluate the climate vulnerability and risk for ∼2,000 species across three ecosystems and 90 fish stocks in the northwest Atlantic Ocean, a documented global warming hotspot. We found that harvested and commercially valuable species were at significantly greater risk of exposure to hazardous climate conditions than non-harvested species, and emissions mitigation disproportionately reduced their projected exposure risk and cumulative climate risk. Of the 90 fish stocks we evaluated, 41% were at high climate risk, but this proportion dropped to 25% under emissions mitigation. Our structured framework demonstrates how climate risk can be operationalized to support short- and long-term fisheries objectives to enhance marine fisheries’ climate readiness and resilience.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".