Estimating mean life of an aged power equipment group with operation data sequence
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper proposes a systematic method of estimating the mean life of an aged power equipment group based on the approach of Dr. W. Li of BCTC, Canada (IEEE Transactions on Power Systems, February 2004). In order to estimate the mean life of aged power equipment systematically and effectively with the operation data sequence, a linear estimation method is developed on the assumption that the stochastic behavior of aged equipment life determines the probability distribution model, such as the normal model or the Weibull model. Introductory and practical examples are illustrated for verification of the proposed approach. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(4): 18–25, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20961
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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