Comment on acp-2022-488
Bibliographic record
Abstract
After the decrease of ozone-depleting substances (ODSs) as a consequence of the Montreal Protocol, it is still challenging to detect a recovery in the total column amount of ozone (total ozone) at northern high-latitudes. To assess regional total ozone changes in the "ozone-recovery"-period (2000–2020) at northern high-latitudes, this study investigates trends from ground-based total ozone measurements at three stations in Norway (Oslo, Andøya, and Ny-Ålesund). For this purpose, we combine measurements from Brewer spectrophotometers, ground-based UV filter radiometers (GUVs), and a SAOZ instrument. The Brewer measurements have been extended to work under cloudy conditions using the global irradiance (GI) technique, which is also presented in this study. We derive trends from the combined ground-based time series with the multiple linear regression model from the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) project. We evaluate various predictors in the regression model and found that tropopause pressure and lower-stratospheric temperature contribute most to ozone variability at the three stations. We report significant positive trends at Andøya (0.9 % per decade) and Ny-Ålesund (1.5 % per decade) and no annual trends at Oslo, but significant positive trends in autumn at all stations. Finally we found positive but insignificant trends of around 3 % per decade in March at all three stations, which may be an indication for Arctic spring-time ozone recovery. Our results contribute to a better understanding of regional total ozone trends at northern high-latitudes, which is essential to assess how Arctic ozone responds to changes in ODSs and to climate change.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.034 | 0.016 |
| Insufficient payload (model declined to judge) | 0.108 | 0.090 |
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".