TOTAL OZONE TRENDS DERIVED FROM THE 14-YEARS MERGED GOME/SCIAMACHY/GOME-2 DATA RECORD
Bibliographic record
Abstract
The stratospheric ozone layer is affected by a variety of factors including natural fluctuations (e.g. the 11-year solar cycle, the equatorial quasi-biennial oscillation, and volcanic eruptions), as well as the emission of ozone depleting substances (ODSs). Although the Montreal Protocol now controls the production and release of those ODSs, the timing of ozone recovery is still unclear. Global long-term observations with space-borne instruments are essential to monitor the further evolution of the stratospheric ozone layer, and they are supplementary to well maintained ground-based measurements. For this study total ozone columns from three European satellite sensors GOME, SCIAMACHY, and GOME-2 are merged into a self-consistent long- term ozone data record starting in 1995. Global ozone trends are then estimated by applying a linear regression model to the merged time series. A global slightly positive trend (<1% per decade) in the total ozone from the last 14 years was found, with marked positive and negative regional patterns. Results are compared to both a second global long-term satellite dataset and to ground-based data. 1. TOTAL OZONE DATASETS
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".