Reported UAV incidents in Canada: analysis and potential solutions
Bibliographic record
Abstract
UAV incidents were analyzed using data from Transport Canada’s Civil Aviation Daily Occurrence Reporting System (CADORS). Between 5 November 2005 and 31 December 2016 a total of 355 incidents were reported in Canadian airspace. The largest number involved UAV sightings (66.5%) and close encounters with piloted aircraft (22.3%). These incidents increased markedly after 2013, with the highest number in British Columbia, followed by Ontario, Quebec, Alberta, and Manitoba. The vast majority of UAV incident reports were filed by pilots of piloted aircraft. Typically, airspace at altitudes greater than 400 feet above ground level (AGL) is off limits to UAVs; however, of the 270 incidents in the CADORS database with UAV altitude reported, 80.4% were above 400 feet AGL and 62.6% were above 1000 feet AGL. Of the 268 incidents with reported horizontal distance to the nearest aerodrome, 74.6% occurred or likely occurred within five nautical miles (M), and of those 92.4% and 76.6% were reported above 100 and 300 feet AGL, respectively. Collectively, the CADORS data indicate that the overwhelming majority of UAV incidents reported in Canada were airspace violations. These results can guide future risk mitigation measures, hardware and software solutions, and educational campaigns to increase airspace safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".