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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".