Carbon footprinting in health systems: one small step towards planetary health
Bibliographic record
Abstract
Climate change is without doubt one of the major threats facing public health. While we are already experiencing extreme weather events worldwide, the longer term impacts on health will include increased heat-related mortality, increased food-borne disease, and increased risk of vector-borne and water-borne disease.1 Coupled with increasing—and mobile—populations and antimicrobial resistance, the pressures on health systems will be substantial. Is it surprising then that the Paris Agreement formally linked human and planetary health so clearly and sought to harness leadership from the health sector to achieve robust change? It is in this light that efforts to assess the carbon footprinting of different elements of health care are needed.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".