SELF-ADAPTIVE OPTIMAL ALLOCATION STRATEGY OF EMERGENCY RESOURCES FOR POWER DISTRIBUTION NETWORK FAILURES, 1-9.
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
When the distribution network fails, emergency resources can provide a reliable guarantee for the rapid and smooth progress of emergency repair work.This study presents a self-adaptive optimal allocation strategy for the emergency resources of power distribution network failures.First, matching functions are established to indicate the matching degree of personnel, vehicles, tools, and spare parts with emergency repair workload.Second, a minimum absolute difference allocation algorithm based on the reasonable allocation of resources is proposed.On this basis, a self-adaptive optimisation allocation strategy for emergency resources is designed.The concept of selfadaptive index is introduced to describe the degree of matching between the emergency repair work and resource allocation as well as the impact of related factors on emergency resource allocation.Finally, the IEEE 33 node system is utilised as a simulation example to verify the effectiveness and practicability of the proposed selfadaptive optimal allocation strategy.
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 | 0.000 | 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 | 0.000 | 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