Canadian experience in the collection of transmission and distribution component unavailability data
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
Equipment and system performance data are usually collected for two basic reasons. The first, and possibly the most obvious reason, is to assess past performance. The second reason is to provide the required information to estimate future performance. Consistent collection of data is essential as it forms the input to relevant reliability models, techniques and equations. Consistent data are required to continuously monitor the performance of an electric power system and to measure its ability to provide reliable service to its customers. Many utilities have established comprehensive procedures for assessing the performance of their systems. In Canada, these procedures have been formulated through the Canadian Electricity Association (CEA). The CEA is an organization for exchanging information on technical, marketing and management problems of mutual interest to its members. In 1975, CEA adopted a proposal to create a facility for centralized collection, processing and reporting of reliability and outage statistics for electric generation, transmission and distribution equipment. The outage statistics made available through this collection and analysis process provide the requisite data to evaluate the reliability of generation, transmission and distribution systems. This paper briefly illustrates the philosophies adopted by Canadian utilities in the collection of component and system outage data. It also presents a summary of the transmission and distribution component unavailability data in the CEA database
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 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