On Depolarization Lidar-Based Method for The Determination of Liquid-Cloud Microphysical Properties
Bibliographic record
Abstract
Under single scattering conditions, water droplets clouds do not depolarize the backscattered light. However, backscattered light from multiple scattering will be depolarized. The level of depolarization is a function of the droplets size, the cloud extinction coefficient value and profile; it has also an important dependency on the lidar field-of-view (FOV). The use of depolarization information to retrieve cloud microphysical properties, using Multiple-FOV has been the object of studies, [1], [2]. Recently the use of the depolarization, at a single FOV, has been studied for cloud with linear liquid water content profiles, [3], [4]. In this paper we present the mechanism leading to depolarization and identify the FOV values for which the information on particle size is high. Also Monte Carlo simulations for cloud with constant and ramp up profiles are presented. The degree of linear depolarization as a function of cloud penetration is significantly different for both cloud profiles. This suggests that the use of the degree of linear depolarization at a single FOV should be used with caution to determine clouds micro-physical parameters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".