Detecting physically unrealistic outliers in ACE-FTS atmospheric measurements
Bibliographic record
Abstract
Abstract. The ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) instrument on board the Canadian satellite SCISAT has been observing the Earth's limb in solar occultation since its launch in 2003. Since February 2004, high resolution (0.02 cm−1) observations in the spectral region of 750–4400 cm−1 have been used to derive volume mixing ratio profiles of over 30 atmospheric trace species and over 20 atmospheric isotopologues. Although the full ACE-FTS level 2 data set is available to users in the general atmospheric community, until now no quality flags have been assigned to the data. This study describes the two-stage procedure for detecting physically unrealistic outliers within the data set for each retrieved species, which is a fixed procedure across all species. Since the distributions of ACE-FTS data across regions (altitude/latitude/season/local time) tend to be asymmetric and multimodal, the screening process does not make use of the median absolute deviation. It makes use of volume mixing ratio probability density functions, assuming that the data, when sufficiently binned, are at most tri-modal and that these modes can be represented by the superposition of three normal, or log-normal, distributions. Quality flags have been assigned to the data based on retrieval statistical fitting error, the physically unrealistic outliers described in this study, and known instrumental/processing errors. The quality flags defined and discussed in this study are now available for all level 2 versions 2.5 and 3.5 data and will be made available as a standard product for future versions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".