Lidar-Based Characterization of the Geometry and Structure of Water Clouds
Bibliographic record
Abstract
Abstract Lidar remote sensing measurements of low-level water clouds in the form of vertical soundings and instantaneous (∼1 min) azimuth-over-elevation scans are reported. Retrievals are made of the liquid water content and effective droplet diameter at the same range, time, and angular resolutions as those of the measurements. The results are presented as time–height plots and two-dimensional horizontal maps of the retrieved parameters. The cloud structure is resolved by calculating histograms, spatial autocorrelation functions, and power spectra. The distribution of the horizontal inhomogeneities is characterized over the size range from 10 to ∼1000 m. The Kolmogorov −5/3 power-law dependence is verified in all cases, but the −5/3 regime is broken into two subregimes that are separated by a sudden increase in the energy density level. The results illustrate how lidars can contribute meaningful information on cloud structure at high spatial and temporal resolutions, in near–real time, and over extended periods of time.
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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) | 0.000 | 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".