Cloud discrimination in probability density functions of limb-scattered sunlight measurements
Bibliographic record
Abstract
Abstract. A technique characterizing the distribution of cirrus cloud-top occurrences from the Optical Spectrograph and Infrared Imaging System (OSIRIS) limb-scattering radiance profiles is presented. The technique involves computing scattering residual profiles by comparing normalized measured radiance and modelled molecular radiance profiles where enhancements in the measured radiance indicate the presence of clouds. Probability density functions of scattering residuals show the distribution is not a continuum measurement; there is a distinction between the cloudy and cloud-free conditions. Observations show high cloud-top occurrences in the upper troposphere and lower stratosphere region above Indonesia and Central America. Results obtained using this technique with OSIRIS measurements are compared to those obtained by Sassen et al. (2008) with Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) nadir measurements and to those obtained by Wang et al. (1996) with Stratospheric Aerosol and Gas Experiment (SAGE) II solar occultation measurements.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".