Detection of in-flight icing conditions through the analysis of hydrometeors with a vertically pointing radar
Bibliographic record
Abstract
In the 100 years since the Wright Brothers first flew a powered aircraft, aviation has not only exploded in popularity, but also has faced many challenges. Since 1931 when icing was first suggested as a possible accident explanation, aircraft icing has been recognized as a significant aviation hazard. Despite extensive research and regulations by the US government, in the last several years (1982 to 2000) there were almost 700 lives and over 450 planes lost in US general aviation accidents where icing played some role (Petty et al., 2003). In the United States alone, an average of 24 accidents, 30 fatalities and 96 million dollars in damage result from icing related accidents each year (Paull and Hagy, 1999). Because of these disturbing statistics, pilots,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".