Managing Disaster Mutual Assistance Operations in Electricity Companies: Developing an ArcGIS Online Web Map Application
Bibliographic record
Abstract
In the event of a large scale disaster, where the damage is apparent, electricity consumers tend to appreciate power outages will occur but their tolerance for the duration of an outage less predictable. As North America advances it technological and knowledge-based economies, its dependency on a safe, uninterrupted and reliable source of electricity reduces its patience for extended power outages. Electricity utilities simply do not have the day-to-day resources necessary to meet the disaster restoration expectations of a customer base dependent on uninterrupted power. Disaster mutual assistance in form of regional and national disaster mutual assistance groups has been used and developed by electricity utilities to support each other during major power outages. Effective management of disaster mutual assistance is a challenging task and requires significant preparedness, coordination, collaboration and resources. Electricity utilities impacted by disaster events need to quickly find other utilities able to provide the necessary assistance, determine their availability and for how long they can assist. Recent large-scale electricity disruptions experienced in Canada and the USA show that traditional methods of organizing disaster mutual assistance may be enhanced and by leveraging emerging technologies. This paper describes the background and motivations behind the development of a disaster mutual assistance coordination tool for Canadian electricity utilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".