Certification and integration aspects of a primary ice detection system
Bibliographic record
Abstract
Recent events transpiring after the crash of a Canadair Regional Jet in Fredericton, New Brunswick, Canada brought into question the ability to certify a primary ice detection system. As the leading supplier of primary ice detection systems, BFGoodrich Aircraft Sensors Division (ASD) has focused much energy to dispel the myths revolving around the certification of these systems. The work presented here addresses a variety of issues ranging from differences in ice detector designs, types of ice detection systems, Ludlam limit issues, ice detector location, system integration and certification issues. From our involvement with the icing community at large and the various aircraft manufacturers, aircraft operators, and pilots, it is apparent more education is needed on how an ice detection system works. This paper addresses these issues with a focus on how each of these pieces fits into the certification of a primary ice detection system. Our experience has shown that many people are insufficiently informed about one or more of these pieces. Understanding the information provided in this paper should provide the groundwork for the certification of a primary ice detection system and its reliable use in service. Some of the benefits of completing the required steps to certify a primary ice detection system are reduced pilot workload, increased fuel savings, and increased safety.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".