Criticality Assessment of Distribution Feeder Sections
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a simple probabilistic method for assessing the criticality of sections of a distribution feeder. The proposed method accounts for a number of factors such as the feeder configuration, reliability performance of each section of the feeder, number of load points of the feeder, and the number of customers at each load points. The various sections of the feeder can be ranked using any one of feeder performance indices. The list of feeder section ranking will enable distribution planners and asset managers to identify the feeder sections that have the most dominant impacts on the overall feeder performance, and therefore, investment decisions can be made effectively towards those sections. An assessment procedure illustrating how the results of the feeder section criticality are used by distribution asset managers is described. The proposed method is illustrated using one of Hydro One's long distribution feeders.
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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