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
Electric power supply grids are vital to social and economic activities as well as to public safety and wellbeing and are ranked as the highest critical infrastructure. There are substantial adverse impacts on society when power grids fail such as disruption to traffic and shut down in the operation of other critical infrastructure elements. This paper presents a novel method to assist in forecasting the probability of power outage based on weather condition in four Canadian provinces—Quebec, Ontario, New Brunswick, and Nova Scotia. System disturbances reports, provided by the North American Electric Reliability Corporation (NERC) from 1992 to 2009, have been scrutinized to determine the conditions that lead to power outage. Based on the reports above, weather condition is found to be a major cause behind power outage that justifies the necessity of a comprehensive study in this area. As a result, a forecasting model for power failure based on weather conditions is developed by artificial neural network (ANN). Once the prototype model is trained, it is able to predict the probability of power outage occurrences by utilizing forecasted weather data for a specific location. Finally, a case study is presented to illustrate the applicability and accuracy of the developed method.
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 | 0.001 | 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 | 0.001 |
| 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