A Comprehensive Reliability Strategy for Managing Assets of Redundant Customer Delivery Systems
Bibliographic record
Abstract
A reliability strategy was developed in 2020 for the purpose of improving the reliability of the dual element spot network (DESN) stations. The strategy utilized the transmission outage data system (TODS) data-base and some reliability models to identify DESN stations with the worst reliability performance so that investment dollars can be directed towards the reliability improvement of those stations. Recently, a comprehensive reliability strategy has been developed to do a similar task of improving the overall reliability of the DESN stations. The new strategy first maps the delivery point interruption data to the TODS outage data to determine the frequencies of the main causes of supply interruptions to delivery points. Then, it identifies action plans (or remedial actions) that are needed in order to reduce the frequencies of the dominant outage causes. Finally, the strategy uses a benefit/cost analysis to select the preferred action plans in order to improve the overall reliability of the DESN stations. The new strategy is more practical and has numerous advantages over the existing one. The purpose of this paper is to describe the new reliability strategy, its advantages over the existing one and to show how to apply it in the DESN stations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".