Skin Cancer Risks Avoided by the Montreal Protocol—Worldwide Modeling Integrating Coupled Climate‐Chemistry Models with a Risk Model for <scp>UV</scp>
Bibliographic record
Abstract
The assessment model for ultraviolet radiation and risk "AMOUR" is applied to output from two chemistry-climate models (CCMs). Results from the UK Chemistry and Aerosols CCM are used to quantify the worldwide skin cancer risk avoided by the Montreal Protocol and its amendments: by the year 2030, two million cases of skin cancer have been prevented yearly, which is 14% fewer skin cancer cases per year. In the "World Avoided," excess skin cancer incidence will continue to grow dramatically after 2030. Results from the CCM E39C-A are used to estimate skin cancer risk that had already been inevitably committed once ozone depletion was recognized: excess incidence will peak mid 21st century and then recover or even super-recover at the end of the century. When compared with a "No Depletion" scenario, with ozone undepleted and cloud characteristics as in the 1960s throughout, excess incidence (extra yearly cases skin cancer per million people) of the "Full Compliance with Montreal Protocol" scenario is in the ranges: New Zealand: 100-150, Congo: -10-0, Patagonia: 20-50, Western Europe: 30-40, China: 90-120, South-West USA: 80-110, Mediterranean: 90-100 and North-East Australia: 170-200. This is up to 4% of total local incidence in the Full Compliance scenario in the peak year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".