The role of open standards in catalysing knowledge transfer to deliver climate adaptive care
Bibliographic record
Abstract
Climate change is threatening many health gains of the last century, with adverse environmental conditions likely to result in an additional 12.5 million deaths annually, and predictions stating that up to four billion people globally will be at risk due to the direct and indirect climate impacts of 2050 1 , 2 . The burden of deaths, diseases, and the socioeconomic and healthcare costs of the climate crisis on lives, livelihoods and livestock, affect those in lower and middle-income countries (LMICs) most deeply, and those with the least resources are the most vulnerable 3 . The differential impacts of climate change on individual health outcomes based on their health history and socioeconomic context require a focused approach to understand and respond to climate-related morbidity and mortality.
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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 | 0.157 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.022 | 0.039 |
| Open science | 0.007 | 0.048 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".