Field trial of an automated ground‐based infrared cloud classification system
Bibliographic record
Abstract
ABSTRACT Automated classification of cloud types using a ground‐based infrared (IR) imager can provide invaluable high‐resolution and localized information for air traffic controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground‐based IR cloud classification system are presented. The system was designed at Campbell Scientific Ltd. in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated IR camera system with a lightning detector in classifying several types of clouds, specifically cumulonimbus and towering cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar‐ and sferics‐automated cloud reports system. In comparisons with METAR data, a probability of detection of up to 82% was achieved, together with a minimum probability of false detection of 18%.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".