Antarctic Polar Stratospheric Cloud Analysis of ACE‐FTS Data From 2005 to 2023
Bibliographic record
Abstract
Abstract We present an analysis of Antarctic polar winters from 2005 to 2023 as observed by the Atmospheric Chemistry Experiment (ACE). The unique broad band infrared spectral features in ACE “residual” spectra are used to classify the spectra of polar aerosols by composition into polar stratospheric clouds (PSCs) and sulfate aerosols. The spectra of PSCs are further classified into nitric acid trihydrate, supercooled ternary solutions, supercooled nitric acid, ice‐mix, and mixtures of PSCs. A breakdown of PSC composition is presented for each year. Antarctic winter seasons with unusual compositions are: 2011, in which volcanic ash mixed with PSCs was observed from July to August; 2019, which experienced a stratospheric warming event; 2020, the PSC season following the Australian Black Summer pyrocumulonimbus event; and 2023, which had unusually large sulfate aerosols following the Honga‐Tonga Honga Ha'apai eruption of 2022.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".