Canadian Certification of Autonomous Flight Systems Working Group (CCAFS WG): summary report FY19 through FY 22
Bibliographic record
Abstract
Over the last number of years Remotely Piloted Aircraft Systems (RPAS) operators, manufactures, regulators and researchers alike have been focusing attention on the development of Beyond Visual Line of Sight (BVLOS) operational capabilities. As the potential for true BVLOS operations comes more plausible and possible in Canada (and around the world) it is apparent that operators will look to gain further efficiencies and enhanced capabilities achievable through autonomous aircraft operations. Likewise there is growing interest in the adoption of increasing levels of automation in traditional aviation platforms as well as newly emerging advanced air mobility (AAM) platforms. Although technical and regulatory challenges remain, research, experimentation and development is already rapidly evolving to focus on autonomous operations of RPAS and Optionally Piloted Vehicles (OPV) in the coming BVLOS environment. In the area of certification, “autonomous1” aircraft operations present a complex challenge in a number of domains, to which traditional approaches applied to aircraft avionics are not well suited. Recent experience with the certification of rotorcraft fly-by-wire systems, amongst many other examples of advanced technology, have highlighted the need for regulators to better understand and anticipate the technical complexities of these rapidly evolving technologies
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".