Three-channel single-wavelength lidar depolarization calibration
Bibliographic record
Abstract
Abstract. Linear depolarization measurement capabilities were added to the CANDAC Rayleigh–Mie–Raman lidar (CRL) at Eureka, Nunavut, in the Canadian High Arctic in 2010. This upgrade enables measurements of the phases (liquid versus ice) of cold and mixed-phase clouds throughout the year, including during polar night. Depolarization measurements were calibrated according to existing methods using parallel- and perpendicular-polarized profiles as discussed in ). We present a new technique that uses the polarization-independent Rayleigh elastic channel in combination with one of the new polarization-dependent channels, and we show that for a lidar with low signal in one of the polarization-dependent channels this method is superior to the traditional method. The optimal procedure for CRL is to determine the depolarization parameter using the traditional method at low resolution (from parallel and perpendicular signals) and then to use this value to calibrate the high-resolution new measurements (from parallel and polarization-independent Rayleigh elastic signals). Due to its use of two high-signal-rate channels, the new method has lower statistical uncertainty and thus gives depolarization parameter values at higher spatial–temporal resolution by up to a factor of 20 for CRL. This method is easily adaptable to other lidar systems which are considering adding depolarization capability to existing hardware.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".