Comment on egusphere-2024-800
Bibliographic record
Abstract
Abstract. A global-scale horizontally- and vertically-resolved ozone climatology can provide a detailed assessment of ozone variability. Here, the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) ozone climatology is improved and updated to the recent decade (1970s–2010s) on a grid of 5° × 5° × 1 km (latitude, longitude, and altitude) from the surface to 26 km altitude, with the most recent ozonesonde data re-evaluated following the ASOPOS-2 guidelines (GAW Report No. 268, 2021). Comparison between independent ozonesonde and trajectory-derived ozone shows good agreement in each decade, altitude, and station, with relative differences (RD) of 2–4 % in the troposphere and 0.5 % in the stratosphere. Comparisons of TOST with aircraft and two satellite datasets, the Satellite Aerosol and Gas Experiment (SAGE) and the Microwave Limb Sounder (MLS), show comparable overall agreement. The updated TOST outperforms the previous version with higher data coverage in all latitude bands and altitudes and 14–17 % lower RD compared to independent ozonesondes, employing twice as many ozonesonde profiles and an updated trajectory simulation model. Higher uncertainties in TOST are where data are sparse, i.e., over the southern high latitudes and the tropics, and before the 1980s, and where variability is high, i.e., at the surface and upper troposphere and lower stratosphere (UTLS). Caution should therefore be taken when using TOST in these spaces and times. TOST captures global ozone distributions and temporal variations, showing an overall insignificant change of stratospheric ozone after 1998. TOST offers users a long record, global coverage, and high vertical resolution.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.461 | 0.348 |
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".