Reevaluation of Total‐Column Ozone Trends and of the Effective Radiative Forcing of Ozone‐Depleting Substances
Bibliographic record
Abstract
Abstract We evaluate total‐column ozone trends using a piecewise linear regression approach and maximizing usage of five gridded total‐column ozone data sets. The new approach yields more consistent estimates of observed ozone loss during 1979–2000, when halocarbon concentrations were increasing, and consequently, using CMIP6 simulations, an increased effective radiative forcing estimate of ozone‐depleting substances with a substantially reduced uncertainty range versus an earlier evaluation. At more than 84% confidence, it is now larger than zero and compares more favorably with three previous evaluations. We furthermore find significant positive post‐1997 global‐ and Southern‐Hemisphere‐mean trends, respectively, in these five data sets. For the extrapolar region ( S– N) and for the Northern Hemisphere, the assessment whether there is a positive trend is inconclusive and depends on which observational data set is included in the calculation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".