An Overview of Applied Methodology in the Gathering and Analysis of Rotorcraft Cold Weather Encounters Data
Bibliographic record
Abstract
Civil and military rotorcraft operators desire enhanced capabilities from their vehicles in terms of mission efficiency, effectiveness, productivity, and availability. A critical element of this challenge is associated with providing cold weather availability. Currently, cold weather operations are enabled by regulatory actions leading to Limited Approvals, Qualifications, Clearances, and Restrictions. Cold weather certification (clearance of a new aircraft) and continuing airworthiness (maintaining effectiveness of fielded aircraft) are data driven processes. This work provides guidance on an Icing Encounters Survey (IES) based data gathering method supporting continuing airworthiness organizations in improving fleet safety and capabilities during cold weather operations.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".