Enhancing disaster mutual assistance decisions with machine learning: case of electricity utilities
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
Disaster mutual assistance (DMA) is an important mechanism that is used by many organisations including electricity utilities to generate the needed resources during major disasters and emergencies. Decision to provide (or not to provide) mutual assistance is a complicated decision that needs to be made considering multiple factors and under time pressure and uncertainty. This paper applies several machine learning algorithms to enhance DMA decisions by electricity utilities. These methods are implemented on an experimental dataset obtained during a workshop participated by disaster management experts from several Canadian electricity utilities. Results show that all of the employed machine learning methods have very high and almost similar accuracy in predicting DMA decisions. However, Random Forest and Decision Tree provide additional information by generating the weight of each criterion, optimum thresholds that can be applied to each criterion, and visual interpretation of the decision process.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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