Multiple-scattering lidar retrieval method: tests on Monte Carlo simulations and comparisons with in situ measurements
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Bibliographic record
Abstract
A multiple-field-of-view (MFOV) lidar measurement and solution technique has been developed to exploit the retrievable particle extinction and size information contained in the multiple-scattering contributions to aerosol lidar returns. We describe the proposed solution algorithm. The primary retrieved parameters are the extinction coefficient at the lidar wavelength and the effective particle diameter from which secondary products such as the extinction at other wavelengths and the liquid-water content (LWC) of liquid-phase clouds can be derived. The solutions are compared with true values in a series of Monte Carlo simulations and with in-cloud measurements. Good agreement is obtained for the simulations. For the field experiment, the retrieved effective droplet diameter and LWC for the available seven cases studied are on average 15% and 35% (worst case) smaller than the measured data, respectively. In the latter case, the analysis shows that the differences cannot be attributed solely to lidar inversion errors. Despite the limited penetration depth (150-300 m) of the lidar pulses, the results of the studied cases indicate that the retrieved lidar solutions remain statistically representative of measurements performed over the full cloud extent. Long-term MFOV lidar monitoring could thus become a practical and economical option for cloud statistical studies but more experimentation on more varied cloud conditions, especially for LWC, is still needed.
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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 it