Bibliographic record
Abstract
Abstract. Two decades of ACE-FTS version 5.2 (v5.2) ozone data (2004–2023) are evaluated with ozonesonde data from across the globe. The biases between the ACE-FTS and ozonesonde measurements are first estimated by analysing coincident data pairs. A second approach is taken for the validation by comparing the ACE-FTS and ozonesonde monthly mean time series, with the former generated by sampling the ACE-FTS data within latitude/longitude boxes (i.e., ±5°/±30°) surrounding the stations and calculating the monthly averages. The biases, correlations, variation patterns and the mean states of the two time series are compared. The biases estimated in this way exhibit more consistent and smoother features than using the coincident pair method. The ACE-FTS and ozonesonde monthly mean time series are highly correlated and exhibit similar variation patterns in the lower stratosphere at all latitudes. The ACE-FTS instrument drifts for each station are assessed in terms of the long-term linear trends relative to ozonesondes, which, although highly stable, may have their own minor changes with time. The ACE-FTS ozone profiles exhibit in general high biases in the stratosphere, increasing with altitude up to ~10 % at around 30 km, and have local maximum differences with ozonesonde profiles at the tropopause heights. The ACE-FTS instrument drifts are generally insignificant overall in the stratosphere with high variation between the stations. Averaging the individual station instrument drifts within several latitude bands results in small insignificant drifts of within ±1 % dec-1 in the northern mid- to high latitudes, and the southern high latitudes, and a small positive insignificant drift of 0–3 % dec-1 in the tropics and southern mid-latitudes with overall uncertainties at 2–3 % dec-1 (2σ level) in the low stratosphere. In the troposphere, the average ACE-FTS instrument drifts vary with altitude and exhibit large drifts between -10 and +10 % dec-1 with uncertainties of 10 % dec-1.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.110 | 0.113 |
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".