Bibliographic record
Abstract
The features of dynamical processes and changes in the ozone layer in the Arctic stratosphere during the winter-spring season 2019–2020 are analyzed using ozonesondes, reanalysis data and numerical experiments with a chemistry-transport model (CTM). Using the trajectory model of the Central Aerological Observatory (TRACAO) and the ERA5 reanalysis ozone mixing ratio data, a comparative analysis of the evolution of stratospheric ozone averaged along the trajectories in the winter-spring seasons of 2010–2011, 2015–2016, and 2019–2020 was carried out, which demonstrated that the largest ozone loss at altitudes of 18–20 km within stratospheric polar vortex in the Arctic in winter-spring 2019–2020 exceeded the corresponding values of the other two winter-spring seasons 2010–2011 and 2015–2016 with the largest decrease in ozone content in recent year. The total decrease in the column ozone inside the stratospheric polar vortex, calculated using the vertical ozone profiles obtained based on the ozonesondes data, in the 2019–2020 winter-spring season was more than 150 Dobson Units, which repeated the record depletion for the 2010–2011 winter-spring season. At the same time, the maximum ozone loss in winter 2019–2020 was observed at lower levels than in 2010–2011, which is consistent with the results of trajectory analysis and the results of other authors. The results of numerical calculations with the CTM with dynamical parameters specified from the MERRA-2 reanalysis data, carried out according to several scenarios of accounting for the chemical destruction of ozone, indicated that both dynamical and chemical processes make contributions to ozone loss inside the polar vortex. In this case, dynamical processes predominate in the western hemisphere, while in the eastern hemisphere chemical processes make an almost equal contribution with dynamical factors, and the chemical depletion of ozone is determined not only by heterogeneous processes on the surface of the polar stratospheric clouds, but by the gas-phase destruction in nitrogen catalytic cycles as well.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.286 | 0.257 |
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".