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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".