Standoff determination of the particle size and concentration of small optical depth clouds based on double-scattering measurements: validation with calibrated target plates and limitations for daytime and nighttime measurements
Bibliographic record
Abstract
Diffractive target plates are used to emulate aerosols of known size and concentration. These target plates are used to validate and determine the sensitivity of a multiple-field-of-view lidar signal inversion technique based on double-scattering measurement to retrieve the particle size and the concentration of small optical depth clouds. We estimate that nighttime and daytime quantification (size and concentration) is possible for optical depths as low as 0.005 and 0.016, respectively. The recovery technique limiting factors are the shot noise, the laser features, the optical lens quality, the background illumination level, the background aerosol fluctuations, and the noise introduced by the lidar detector, a gated intensified camera (camera G-ICCD).
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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".