<title>Wide-angle imaging lidar (WAIL): a ground-based instrument for monitoring the thickness and density of optically thick clouds</title>
Bibliographic record
Abstract
Traditional lidar provides little information on dense clouds beyond the range to their base (ceilometry), due to their extreme opacity. At most optical wavelengths, however, laser photons are not absorbed but merely scattered out of the beam, and thus eventually escape the cloud via multiple scattering, producing distinctive extended space- and time-dependent patterns which are, in essence, the cloud's radiative Green functions. These Green functions, essentially 'movies' of the time evolution of the spatial distribution of escaping light, are the primary data products of a new type of lidar: Wide Angle Imaging Lidar (WAIL). WAIL data can be used to infer both optical depth and physical thickness of clouds, and hence the cloud liquid water content. The instrumental challenge is to accommodate a radiance field varying over many orders of magnitude and changing over widely varying time-scales. Our implementation uses a high-speed microchannel plate/crossed delay line imaging detector system with a 60-degree full-angle field of view, and a 532 nm doubled Nd:YAG laser. Nighttime field experiments testing various solutions to this problem show excellent agreement with diffusion theory, and retrievals yield plausible values for the optical and geometrical parameters of the observed cloud decks.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".