Bibliographic record
Abstract
Abstract. Ozone depletion over the polar regions is monitored each year by satellite and ground-based instruments. In this study, the vortex-averaged ozone loss over the last three decades is evaluated for both polar regions using the passive ozone tracer of the chemical transport model TOMCAT/SLIMCAT and total ozone observations from Système d'Analyse par Observation Zénithale (SAOZ) ground-based instruments and Multi-Sensor Reanalysis (MSR2). The passive tracer method allows us to determine the evolution of the daily rate of column ozone destruction, and the magnitude of the cumulative loss at the end of the winter. Three metrics are used to estimate the linear trend since 2000 and to assess the current situation of ozone recovery over both polar regions: 1) The maximum ozone loss at the end of the winter; 2) the onset day of ozone loss at a specific threshold and 3) the ozone loss residuals computed from the differences between annual ozone loss and ozone loss values regressed with respect to sunlit volume of polar stratospheric clouds (VPSC). This latter metric is based on linear and parabolic regressions for ozone loss in the Northern and Southern Hemispheres, respectively. In the Antarctic, metrics 1, and 3, yield trends of -2.3 and -1.8 % dec-1 for the 2000–2021 period, significant at 1 and 2 standard error (σ), respectively. For metric 2, various thresholds were considered, all of them showing a time delay for when they are reached. The trends are significant at the 2σ level and vary from 3.5 to 4.2 day dec-1 between the various thresholds. In the Arctic, metric 1 exhibits large interannual variability and no significant trend is detected; this result is highly influenced by the record ozone losses in 2011 and 2020. Metric 2 is not applied in the Northern Hemisphere due to the difficulty of finding a threshold value in a consistent number of winters. Metric 3 shows a negative trend in Arctic ozone loss residuals of -1.7 ±1 % dec-1, significant at 1σ level. This is therefore the first quantitative detection of ozone recovery in the Arctic springtime lower stratosphere.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.260 | 0.236 |
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".