Urban airflow: what drone pilots need to know
Bibliographic record
Abstract
Within an urban environment, a remotely piloted aircraft system (RPAS) flight plan must account for restrictions in pathways, loss of visual line of sight due to buildings, a limited number of emergency safe landing locations, and avoidance of populated areas. Adding to the complexity of navigating around the city structures are urban wind characteristics caused by interactions between wind and the structures. For RPAS to operate safely in an urban environment, the effects that various forms of airflow have on their controllability and hence flight path, may be of concern. Without detailed knowledge of specific airflow patterns for a Canadian city or the effect that the airflow has on a specific RPAS, a starting point for preparing RPAS users for urban wind conditions is to provide awareness of types of urban airflow and where/when that airflow type may occur. To support Transport Canada (TC) promotion of safe operation of RPAS within the urban environment during the early stage of the regulatory development process, an RPAS user awareness video (Urban Airflow: What Drone Pilots Need to Know) on urban airflow characteristics was provided to TC by the NRC in both English and French languages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".