The regulatory approach of ICAO, the United States and Canada to Civil Unmanned Aircraft Systems in particular to certification and licensing
Bibliographic record
Abstract
Civil Unmanned Aircraft Systems (UAS) have increased in variety and importance. They offer applications that can replace manned aircraft in certain areas or that are unprecedented by their manned counterparts and unique to UAS. The current national and international regulatory framework for aviation regulates 'aircraft' and does hence generally not differentiate between manned and unmanned formats. However, most of its regulations were developed in the light of manned aircraft making their application to UAS a difficult task. The potential of UAS has been recognized, work on future regulations is underway and the first legal instruments aiming for UAS integration have been developed. This thesis explains and contrasts the regulatory approaches of the International Civil Aviation Organization (ICAO), the United States and Canada to UAS. Present rules and proposals for future regulations are analyzed. In a closer look, the actual certification and licensing rules for UAS and their resultant operational possibilities are examined and compared.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".