Airborne elastic cloud lidar for ice-water content retrievals during the HAIC-HIWC 2015 campaign
Bibliographic record
Abstract
In 2014, the National Research Council (NRC) and Environment and Climate Change Canada (ECCC) acquired two Airborne Elastic Cloud Lidar (AECL) systems produced by Alpenglow Instruments LLC, for the purposes of retrieving in-flight vertical profiles of clouds and aerosols. In May 2015, the AECLs were deployed on board the NRC Convair-580 aircraft during the HAIC-HIWC (High Altitude Ice Crystals – High Ice Water Content) campaign in French Guiana, where they were used to acquire nearly 40 hours of measurements of various atmospheric features, including liquid and mixed phase clouds as well as ice crystals in HIWC regions. The present report has a twofold objective. First, we describe the AECL operating principle, technical specifications as well as data processing chain. Particular attention is given to the problem of incomplete overlap between the laser and the receiving telescope fields of view. A Klett backward inversion method is also described and is used to convert the raw lidar return into the backscatter and extinction coefficients based on several assumptions, including an assumed extinction-to-backscatter ratio. The second objective of the report is to showcase some of the AECL cloud characterization capabilities based on representative test cases from the HAIC-HIWC campaign.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".