A comparison of two novel approaches for conducting detect and avoid flight test
Bibliographic record
Abstract
This paper compares two approaches developed by the National Research Council of Canada to conduct “near-miss” intercepts in flight test, and describes a new method for assessing the efficacy of these trajectories. Each approach used a different combination of flight test techniques and displays to provide guidance to the pilots to set-up the aircraft on a collision trajectory and to maintain the desired path. Approach 1 only provided visual guidance of the relative azimuth and position of the aircraft, whereas Approach 2 established the conflict point (latitude/longitude) from the desired geometry, and provided cross track error from the desired intercept as well as speed cueing for the arrival time. The performance of the approaches was analyzed by comparing the proportion of time where the predicted closest approach distance was below a desired threshold value. The analysis showed that Approach 2 resulted in more than double the amount of time spent at or below desired closest approach distance across all azimuths flown. Moreover, since less time was required to establish the required initial conditions, and to stabilize the flight paths, the authors were able to conduct 50% more intercepts.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".