Estimating Maximum Aircraft Icing Environments Using a Large Database of In-Situ Observations
Bibliographic record
Abstract
A large data base of in-situ aircraft icing observations collected during five field campaigns with two research aircraft is used to assess the 99 and 99.9% liquid water content (LWC) values associated with icing environments. Icing environments assessed include those with drops smaller than 100 μm and those with supercooled large drops (SLD) larger than 100 μm. The low probability LWC values were calculated using an extreme value analysis technique, and the results were compared to those obtained by assuming that the LWC observations could be fitted to exponential, gamma or Weibull distributions. Extreme value analysis allows quantification of the nature of distributions in the tails of the distributions, and hence provides a more accurate method for determining extreme values and their associated confidence limits. The results are compared to the icing envelopes from the Federal Aviation Administration Regulation 25 Appendix C and with other icing envelopes. Scale factors for computation of 99 and 99.9% LWC values for icing environments at horizontal length scales larger than 3 km are also determined.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".