Total ozone loss during the 2021/22 Arctic winter and comparison to previous years
Bibliographic record
Abstract
The amplitude and rate of ozone depletion in the Arctic is monitored every year since 1994 by comparison between SAOZ UV-Vis ground-based network from NDACC and Multi-Sensor Reanalysis 2 (MSR-2) total ozone measurements over 8 stations in the Arctic and 3-D chemical transport model simulations in which ozone is considered as a passive tracer. The passive ozone method allows determining the cumulative loss at the end of the winter. The amplitude of the destruction varies between 0-10% in relatively warm and short vortex duration years to 25-38% in colder and longer ones, which the record winters estimated in 2010/2011 and 2019/2020. In this study, the interannual variability of 10-days average rate of 2021/2022 winter will be analyzed and compared to previous years. In addition, SAOZ NO2 data will be used to evaluate re-noxification in the Arctic. The long-term ozone loss series estimated from measurements will be compared to REPROBUS and SLIMCAT CTM simulations. Relationship with illuminated Polar Stratospheric Clouds will be also presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".