Bibliographic record
Abstract
At the 1972 International Helicopter Ice Protection Conference it was noted that “New York Airways is shut down an average of one day/month in the winter due to forecast icing,” that “The Canadian Navy finds its operations restricted by 25 percent of the time off the coast of Nova Scotia in the winter,” and “approximately 7 percent of the helicopter training flights in the United Kingdom are curtailed due to forecast icing,”. This is quite significant, especially for low altitude rotorcrafts. Bell Helicopter manufactures and offers a wide range of helicopter models and Tiltrotors. Bell Helicopter has accumulated a large database of experience in research, design and development of ice protection systems for rotorcraft. All Bell Helicopter commercial models propulsion systems and most of the military models are certified/qualified for operation in icing. The model 412 and 214ST have also been tested at the NRC spray rig, behind the Helicopter Icing Spray System (HISS) and in natural conditions. The V-22 Tiltrotor has been tested behind the HISS, the NKC-135 icing spray tanker and natural conditions. The Bell/Agusta BA609 is following the tradition of rigorous testing set by the previous programs in its process of full FAA icing certification. This paper summarizes the many Bell Helicopter icing projects conducted throughout the years through flight test on helicopters as well as analytical and model testing research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".