Review of Metrics and Assignment of Confidence Intervals for Health Management of Gas Turbine Engines
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 development and evaluation of new diagnostic systems requires statistically-based methods to measure performance. Various metrics are in use by developers and users of diagnostic systems. Current metrics practices are reviewed, including receiver operating characteristics, confusion matrices, Kappa coefficients and various entropy techniques. A set of metrics is then proposed for assessment of diverse gas path diagnostic systems. The use of bootstrap statistics to compare metric results is developed, and demonstrated for a set of hypothetical data sets with a range of relevant characteristics. The bootstrap technique allows the expected range of the metric to be assessed without assuming a probability distribution. A method is proposed to develop confidence intervals for the calculated metrics. The application of a confidence interval could prevent a good diagnostic technique being discarded because of a lower value metric in one test instance. The strengths and weaknesses of the various metrics with derived confidence intervals are discussed. Recommendations are made for further work.
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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