Bibliographic record
Abstract
Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. To increase confidence in the load estimation process, ensemble methods are employed combining multiple individual load estimators that increase predictive stability across flights and add robustness to noisy data. In this work, several load estimation methods are applied to a variety of machine learning algorithms to build a large library of individual load estimation models for main rotor yoke loads from 28 flight state and control system parameters. This paper explores several ensemble integration methods including simple averaging, weighted averaging using rank sum, and forward selection. From the 426 individual models, 25 top models were selected based on four ranking metrics, root mean squared error (RMSE), correlation coefficient, and interquartile ranges of these two metrics. All ensembles achieved improved performance for these four metrics compared to the best individual model, with the forward selection ensemble obtaining the lowest RMSE, highest correlation, and closest load signal prediction visually of all models.
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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".