Climate policy action needed to reduce vulnerability of conservation‐reliant grassland birds in North America
Bibliographic record
Abstract
Grassland birds have suffered dramatic population declines and are under threat of further grassland conversion. Simultaneously, grassland regions are projected to have high rates of future climate change. We assessed the vulnerability of grassland birds in North America under scenarios of global climate change reflecting the objectives of the Paris Agreement. The assessment incorporated model‐based projections of range losses and gains as well as trait‐based information on adaptive capacity. Nearly half (42%) of grassland birds were highly vulnerable during the breeding season under a 3.0°C increase in global mean temperature scenario representing current commitments under the Paris Accord. This proportion declined to 13% with a 2.0°C increase and to 8% with a 1.5°C increase over preindustrial global mean temperature. Regardless of scenario, more than 70% of grassland birds had some vulnerability to climate change. Policy actions beyond the present‐day national commitments under the Paris Accord are needed to reduce vulnerability of grassland birds in a changing climate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".