Non-intrusive Flight Test Instrumentation using Video Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The National Research Council of Canada has conducted feasibility studies into the development of non-intrusive flight test instrumentation methods with the goal of reducing the cost and time-to-market for certified aerospace products. Video recognition for the collection of flight test time history data was one such non-intrusive method. The advantages of using machine vision for flight data collection are many. One video camera can be used to extract data for many in-flight parameters, reducing instrumentation time, the airworthiness effort, the overall aircraft schedule and associated costs. This paper details the development of flight test video recognition software, calibration algorithms, hardware, and the accuracy of data collected by video via full flight simulator data benchmarks. Video recognition is a convenient means of collecting cockpit flight test data for model development and certification of full flight simulator devices.
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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 it