Bibliographic record
Abstract
Abstract. Depletion of the stratospheric ozone layer remains an ongoing environmental issue, with increasing stratospheric chlorine from Very Short-Lived Substances (VSLS) recently emerging as a potential but uncertain threat to its future recovery. Here the impact of chlorinated VSLS on past ozone is quantified, for the first time. using the UM-UKCA chemistry-climate model. Model simulations show that between 2010–2019 Cl-VSLS reduced total column ozone by, on average, ~2–3 DU in the springtime high latitudes and by ~0.5–1 DU in the tropics, with up to 5–6 DU monthly and zonal mean Arctic ozone reductions simulated in the springs of 2011, 2014 and 2020. Cl-VSLS impacts during the recent cold Arctic winter of 2019/2020 are also quantified to have resulted in up to 6 % reduction of lower stratospheric ozone and ~6 DU ozone in total by the end of March. On the other hand, the simulations show that the inclusion of Cl-VSLS does not considerably modify the magnitude of the diagnosed recent ozone trends. We also estimate the ozone depletion potential of dichloromethane, the most abundant Cl-VSLS, at 0.0107. Our results thus illustrate a so-far modest but nonetheless non-negligible role of Cl-VSLS in contributing to stratospheric ozone budget over the recent past that if to continue could offset some of the gains achieved by the Montreal Protocol.
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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.367 | 0.211 |
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".