Bibliographic record
Abstract
Abstract. We develop an algorithm that uses the aerosol extinction at two wavelengths (color ratio) to derive the size and number density for stratospheric aerosols. We apply our algorithm to Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP) L2 and Stratospheric Aerosol and Gas Experiment (SAGE) data. We show that the color ratio between two wavelengths (e.g. 510 nm/869 nm) is insensitive to aerosol concentration and thus can be used to derive aerosol size assuming a log-normal size distribution. With the size and the extinction, we can compute a number density consistent with both wavelengths. Our results compare favorably to balloon borne particle size and concentration measurements. Our results are also consistent with SAGE solar occultation measurements. Finally, we show the background distribution of stratospheric aerosols and the changes in those distributions during the Reikoke and Hunga Tonga-Hunga Ha’apai volcanic eruptions. We also show the evolution of the size and number density of aerosols following both of those eruptions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.011 |
| Insufficient payload (model declined to judge) | 0.162 | 0.158 |
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".