First results from the Canadian Convair (NRC) 35 GHz cloud-profiling radar during AIRS
Bibliographic record
Abstract
The NRC Institute for Aerospace Research and the Meteorological Service of Canada have collaborated for a number of years in making atmospheric measurements using the NRC Convair 580 aircraft. This has involved measurements of physical parameters including atmospheric state, cloud particle microphysics and solar radiation. In recent years an IR lidar system with dual beams has been installed. During 1999, a cloud-profiling radar (CPR), operating at a wavelength of 8.6 mm, was installed to make additional remote measurements. This millimetre-wave radar system provides a unique capability when used in concert with other sensors such as the lidar and A-Band spectrometer. During December 1999 and January/February 2000, the radar was used for the first time in the Alliance Icing Research Study (AIRS). The primary focus of the study was to measure atmospheric conditions leading to aircraft icing, using both in-situ as well as remote measurements from the aircraft and the ground. The purpose of this paper is to describe the radar and lidar and other related aircraft sensors, and to show some preliminary results from the AIRS experiment. The motivation for using a cloud radar on the aircraft was to make measurements of radar reflectivity (and eventually Doppler spectra) at high sensitivity and spatial resolution.
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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) | 0.001 | 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 | 0.001 | 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".