<title>Lidar depolarization asymmetry measurements and its relation with the optical depth</title>
Bibliographic record
Abstract
We measure the cross-polarized backscattered light from a linearly polarized laser beam penetrating a cloud made of spherical particles with a gated intensified CCD camera. In accordance with previously published results, we observe a clear azimuthal pattern in the recorded images. We show that the pattern originates from second order scattering, and that higher-order scattering causes blurring that increases with optical depth. We also find that the contrast of the symmetrical features can be related to the measure of the optical depth. Moreover, by identifying and subtracting the blurring contributions, the resulting pattern provides a "pure" second-order scattering measurement that can be used for the retrieval of droplet size. We apply this technique on a stratus cloud located at 1400 m. The extinction values retrieved on the basis of the laboratory quantification of the blurring of the multiple scattering secondary polarization patterns measured from the ICCD images are then compared with the profile of the extinction coefficient obtained using Bissonnette's algorithm, which is based on the multiple-field-of-view (MFOV) lidar returns.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".