The Grey Clustering Analysis for Photovoltaic Equipment Importance Classification
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 evaluation of equipment importance is an important part of the operation and maintenance management and its purpose is to identify the key equipment which has important influence on the reliable operation of the power station. On the basis of grey clustering theory, the paper firstly establishes the analysis model of photovoltaic (pv) power station equipment importance and the weights of factors in the target hierarchy is solved by using the judgment matrix in analytic hierarchy process. Secondly, according to the whitenization weight function of the grey clustering, pv equipment is classified into three types: key equipment, vital equipment and general equipment, which lays the foundation for the equipment maintenance strategy choice. Finally, an application example verifies the practicality and scientificity of the proposed method.
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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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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