Monte Carlo approach to identification of the composition of stratospheric aerosols from infrared solar occultation measurements
Bibliographic record
Abstract
We describe an inversion method for determining the composition, density, and size of stratospheric clouds and aerosols by satellite remote sensing. The method, which combines linear least-squares minimization and Monte Carlo techniques, is tested with pure synthetic IR spectra. The synthetic spectral data are constructed to mimic mid-IR spectra recorded by the Improved Limb Atmospheric Spectrometer (ILAS-I and ILAS-II) instruments, which operate in the solar occultation mode and record numerous polar stratospheric cloud events. The advantages and limitations of the proposed technique are discussed. In brief we find that stratospheric aerosol in the size range from 0.5 to 4.0 02114 microm can be retrieved to an accuracy of 30%. We also show that the chemical composition of common stratospheric aerosols can be determined, whereas identification of their phases from mid-IR satellite remote-sensing data alone appears to be questionable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".