In-Flight Test Campaign to Validate PIO Detection and Assessment Tools
Bibliographic record
Abstract
This paper describes a joint research campaign conducted by the German Aerospace Center (DLR) and the National Research Council Canada (NRC) to explore methods and techniques to expose rotorcraft Pilot-Induced Oscillations (PIOs) during flight testing. A flight test campaign was conducted at NRC using the Bell 205 experimental aircraft. Particularly for the lateral axis, ADS-33 tasks can be successfully applied to expose PIO tendencies. Novel subjective and objective criteria were used during the test campaign. PIO prediction boundaries of the objective phase-aggression critieria (PAC) detection algorithm were validated through results obtained. This was the first use of PAC with data recorded in-flight. To collect subjective feedback, the aircraft pilot coupling (APC) scale was used. This was the first use of the novel scale in-flight and received favorable feedback from the evaluation pilot. Modifications to ADS-33 mission tasks were found to successfully improve the ability to consistently expose PIOs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".