Bibliographic record
Abstract
Ozone hole is one of the most striking and influential metaphors in contemporary environmental discourse. Not an actual “hole” as such, it refers to a progressive, seasonal thinning of ozone concentrations in the lower stratosphere provoked by a buildup of chlorine‐based compounds emitted by refrigerators, air conditioners, and aerosol spray cans. This results in the destruction of ozone molecules that protect us from ultraviolet radiation from the sun. Harmful effects include an elevated risk of skin cancer, cataracts, and a weakening of the human immune system. The ozone hole was first dramatically depicted in an animated video created from longitudinal NASA satellite data, showing a precipitous decline in ozone concentrations over the Antarctic since 1960. The image of a “hole in the ozone” resonated widely with journalists, politicians, and the public. It played a central role in the passing of the 1987 Montreal Protocol on Substances that Deplete the Ozone Layer, widely described as the most successful multilateral environmental agreement ever. Ozone levels in the stratosphere stabilized at the beginning of the millennium, but have since started to rise again, partly because the replacements for chemical compounds banned by the Montreal Protocol have proven to be less benign than expected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".