Arctic stratospheric ozone depletion and increased UVB radiation: potential impacts to human health
Bibliographic record
Abstract
Contrary to popular belief, stratospheric ozone depletion, and the resultant increase in solar UV-B (280-320 nm), are unlikely to fully recover soon. Notwithstanding the success of the Montreal Protocol in reducing the amount of ozone destroying chemicals into the stratosphere, the life-times of these compounds are such that even with full compliance with the Protocol by all countries, it will be decades before stratospheric ozone could return to pre-1980 levels. This raises the question, therefore, of what will happen to biological processes essential to the maintenance of life on earth which are sensitive to damage by increased UV-B radiation, particularly those involved with human health? The polar regions, because of the vagaries of climate and weather, are the bellwether for stratospheric ozone depletion and will, therefore, be the first to experience impacts due to increases in solar UV-B radiation. The impacts of these are incompletely understood and cannot be predicted with certainty. While some UV-B impacts on human health are recognized, much is unknown, unclear and uncertain. Thus, this paper attempts, as a first approximation, to point out potential impacts to the health and welfare of human inhabitants of the Arctic due to increased solar UV-B radiation associated with stratospheric ozone depletion. As will be seen, much more data is critically needed before adequate risk assessment can occur.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".