Using cell-DEVS for prototyping unmanned aircraft system traffic simulation
Bibliographic record
Abstract
The use of Unmanned Aerial Systems (UASs) is expanding speedily. This results in a need to integrate UAS traffic into non-segregated airspace. However, this integration introduces risks of mid-air collisions between UASs and manned aircraft (MA) in the airspace. To deal with these issues, we present two models that work together to assess the risk of a mid-air collision between a UAS and another manned aircraft operating in Canada's Northern airspace. The first model represents a part of Canada's Northern airspace and the aircraft and UAS traffic in it. The second model is an Uncorrelated Encounter Model (UEM) which determines whether or not a mid-air collision has occurred between a UAS and an aircraft. These two models are integrated to form a UAS-UEM model which determines the number of mid-air collisions in several UAS and aircraft flight simulations. Our results show a low probability for a mid-air collision between a UAS and an aircraft in the airspace region of interest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".