Improved multi-spectral polarimetric observations of UTLS aerosol and cloud from stratospheric balloon with the Aerosol Limb Imager
Bibliographic record
Abstract
The Aerosol Limb Imager (ALI) is a multi-spectral imager capable designed to observe aerosol extinction and particle size profiles in the upper-troposphere lower-stratosphere. ALI uses a system of linear polarizers, a liquid crystal rotator, and an acoustic-optic tunable filter to select the linear polarization state and wavelength of limb scattered sunlight radiance between 600 nm and 1500 nm. From stratospheric balloon, spectral images have spatial resolution of <100 meters at the tangent point, and can produce useful aerosol observations between 5 km and 30 km in altitude. Of novelty is the polarimetric capability of ALI, which uses the orthogonal polarization states to detect cloud in the spectral data and facilitate its distinction from aerosol. Two previous iterations of the ALI instrument concept have already been successfully demonstrated, once in 2014 and again in 2018. Currently, a third iteration is being developed which improves upon the thermal, structural, and optical performance of the previous iterations. This improved iteration is scheduled for demonstration as part of the HEMERA program out of Kiruna, Sweden in the summer of 2021. This demonstration serves the larger objective of further proving the engineering and scientific readiness of the ALI instrument concept for eventual high-altitude aircraft and satellite platform deployments. ALI is a proposed Canadian contribution to the NASA A-CCP satellite mission study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".