Climate change and lung health: the challenge for a new president
Bibliographic record
Abstract
This is a story from recent history that we believe incoming President Trump urgently needs to hear. In 1985, a huge and growing hole in the planet's ozone layer was identified. Ozone in the stratosphere blocks some of the sun's ultraviolet radiation from reaching the Earth's surface, thus protecting its biosphere (which includes humans) from DNA damage that would otherwise occur. The hole was caused by the action of chlorofluorocarbons (CFCs) used in refrigeration and as aerosol propellants.1 Margaret Thatcher is admired by many on both sides of the Atlantic, including President Trump2 as a strong politician, a person with clear beliefs on which she acted; the ‘Iron Lady’ as Donald Trump has described her on Twitter. A scientist by background, Thatcher appreciated the magnitude of the threat immediately, throwing her weight behind international efforts to address this. Working with Ronald Reagan, within 2 years the 1987 Montreal Protocol was signed to phase out the production and use of CFCs. Despite this, because CFCs persist in the atmosphere for more than a century, the ozone layer will not recover completely until 2060. A failure of leadership at that time would have been catastrophic. CFCs are also powerful greenhouse gases and without the Montreal agreement the world would already …
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".