Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: experiment
Bibliographic record
Abstract
We present effective radius, volume, surface-area, and number concentrations as well as mean complex refractive index of tropospheric particle size distributions based on lidar measurements at six wavelengths. The parameters are derived by means of an inversion algorithm that has been specifically designed for the inversion of available optical data sets. The data were taken on 20 June and on 20 July 1997 during the Aerosol Characterization Experiment ACE 2 (North Atlantic/Portugal) and on 9 August 1998 during the Lindenberg Aerosol Characterization Experiment LACE 98 (Lindenberg/Germany). Measurements on 20 June 1997 were taken in a clean-marine boundary layer, and a large value of 0.64 µm for the effective radius, a low value of 1.45 for the real part, and a negligible imaginary part of the complex refractive index were found. The single-scatter albedo was 0.98 at 532 nm. It was derived from the particle parameters with Mie-scattering calculations. In contrast, the particles were less than 0.2 µm in effective radius in a continental-polluted aerosol layer on 20 July 1997. The real part of the complex refractive index was ∼1.6; the imaginary part showed values near 0.03i. The single-scatter albedo was 0.84. On 9 August 1998 an elevated particle layer located from 3000 to 6000 m was observed, which had originated from an area of biomass burning in northwestern Canada. Here the effective radius was ∼0.24 µm, the real part of the complex refractive index was above 1.6, the imaginary part was ∼0.04i, and the single-scatter albedo was 0.81. Excellent agreement has been found with results based on sunphotometer and in situ measurements that were performed during the field campaigns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".