Maneuver Generation for Regime Recognition Design and Validation
Bibliographic record
Abstract
Accurate characterization of fleet and individual aircraft usage spectrums would allow component retirement times to be based on actual aircraft usage rather than on an assumed worst case usage spectrum used in a traditional time-based maintenance approach. A key enabling technology for such a Usage or Condition Based Maintenance (UBM/CBM) program is Regime Recognition (RR). The development and validation of such algorithms commonly employs flight loads survey data, which captures critical regimes and the corner points of the operational envelope. Such maneuver examples are flown precisely and don’t necessarily capture how fleet aircraft are flown. The maneuver generation approach presented herein presents the foundational elements of a methodology to augment existing flight loads survey data with statistically independent maneuvers that address the desire to capture the variation that would be observed in the fleet as a result of different pilots, vehicle load-out, and environmental conditions via a simulation based autopilot. Such a capability allows developers to cost-effectively conduct comprehensive sensitivity analyses and verification of RR algorithms and the end-to-end process, while reserving high-cost flight test data for true blind validation.
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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".