Assessment of the quality of ACE-FTS stratospheric ozone data
Bibliographic record
Abstract
Abstract. For the past 17 years, the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument on the Canadian SCISAT satellite has been measuring profiles of atmospheric ozone. The latest operational versions of the level 2 ozone data are versions 3.6 and 4.1. This study characterizes how both products compare with correlative data from other limb-sounding satellite instruments, namely MAESTRO, MLS, OSIRIS, SABER, and SMR. In general, v3.6, with respect to the other instruments, exhibits a smaller bias (which is on the order of ∼ 3 %) in the middle stratosphere than v4.1 (∼ 2 %–9 %); however, the bias exhibited in the v4.1 data tends to be more stable, i.e. not changing significantly over time in any altitude region. In the lower stratosphere, v3.6 has a positive bias of about 3 %–5 % that is stable to within ±1 % per decade, and v4.1 has a bias on the order of −1 % to +5 % and is also stable to within ±1 % per decade. In the middle stratosphere, v3.6 has a positive bias of ∼ 3 % with a significant negative drift on the order of 0.5 %–2.5 % per decade, and v4.1 has a positive bias of 2 %–9 % that is stable to within ±0.5 % per decade. In the upper stratosphere, v3.6 has a positive bias that increases with altitude up to ∼ 16 % and a significant negative drift on the order of 2 %–3 % per decade, and v4.1 has a positive bias that increases with altitude up to ∼ 15 % and is stable to within ±1 % per decade. Estimates indicate that both versions 3.6 and 4.1 have precision values on the order of 0.1–0.2 ppmv below 20 km and above 45 km (∼ 5 %–10 %, depending on altitude). Between 20 and 45 km, the estimated v3.6 precision of ∼ 4 %–6 % is better than the estimated v4.1 precision of ∼ 6 %–10 %.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".