Unmanned Aircraft System (UAS) Integration to Airspace and Collision Risk Assessment
Bibliographic record
Abstract
Near Mid-Air Collision (NMAC) risk is an impediment to the integration of Unmanned Aircraft System (UAS) into non-segregated airspace. It is quantified by a NMAC rate per flight hour, and this rate has been used for risk assessment for manned aviation. To estimate similar statistics with the inclusion of UAS in a known airspace, MIT Lincoln Laboratory (MIT/LL) has built an UAS-centric encounter model of the US National Airspace System (NAS). This thesis builds upon the MIT/LL encounter model to estimate a Canadian NMAC rate using on a standard proposed by National Research Council of Canada. The reported assessment takes into the account of the variation of field of view, radar range used for detection and tracking of the Intruder and the UAS achievable horizontal turn rate. Depending on the traffic, a notional radar-based detect-and-avoidance system could be demonstrated to have a lower NMAC rate than the Canadian NMAC rate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".