Climate change mitigation and synergies with primary cancer prevention in Europe: time to implement opportunities
Bibliographic record
Abstract
Ten years after the adoption of the treaty on climate change by the 21st Conference of the Parties in Paris, implementation of climate change mitigation measures remains a priority and urgency. The same priority and urgency apply to cancer prevention to counter the trend of an increasing cancer burden. The burden is projected to increase worldwide more than 50% during the next 20-25 years, ruling out treatment as the only countermeasure because of overburdened health systems. Although the effects of global warming on the cancer burden are highly speculative, synergies of remedial action on climate change and increasing cancer rates have clearer evidence base. These synergies are described for the situation in Europe using the fourth edition of the European Code Against Cancer for recommendations on cancer prevention and the 2030 breakthroughs for climate change mitigation by the United Nations Climate Change High-Level Champions Climate Solutions Implementation Roadmap. European Code Against Cancer's recommendations on healthy body weight, physical activity, reduced meat consumption, avoiding too much sun, and reducing air pollution align well with many of the 2030 breakthrough recommendations on healthier food including limiting meat consumption; on cleaner air through reducing transportation and in general reducing carbon, methane, and other emissions; and on mitigating temperature rise. Campaigns combining climate change mitigation with cancer prevention have the potential to encourage individuals, community groups, and policymakers to empower the implementation of measures both for a healthy planet and toward a world where fewer people get cancer.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".