Ozone recovery and climate change: Towards an interactive \nrepresentation of stratospheric ozone in Earth System \nModels
Bibliographic record
Abstract
Interactions between climate change and stratospheric ozone modify \nboth, the evolution of surface climate and the recovery of the stratospheric \nozone layer. Accounting for the climate feedbacks from changing \nozone as well as the impact of climate change on the evolution of \nthe ozone layer requires the interactive representation of stratospheric \nchemistry in Earth System Models. \nOur understanding of stratospheric ozone chemistry is now mature \nat the process scale and state of the art Chemical Transport Models \n(CTM) result in a realistic representation of the global ozone layer and \nthe chemical processes affecting it. But the huge computational effort of \nthese models makes it difficult to include the ozone layer interactively \nin Earth System Models (ESMs). \nWe have developed SWIFT, an extremely fast module for interactive \nozone chemistry in climate models. SWIFT allows for an interactive \ntreatment of stratospheric ozone in standard ESMs with little numerical \noverhead. We will present the current status of SWIFT and results \nfrom coupling SWIFT to a climate model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".