Visualization and support tool of power system restoration using hierarchical Color Petri Net
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
Rapid restoration is vital for power system restoration to mitigate the consequences of major blackout. However, conventional textual restoration procedure hides the relations between conditions and the restoration actions, tends to slow down the restoration process and make the restoration harder. This paper proposed a visualization and support tool of restoration process with Hierarchical CPN (Color Petri Net). This tool graphically represents the relations among the system conditions and restoration activities, and gives explicit see of the status during the restoration process. The advantages of the proposed tool are (1) restoration areas or major restoration activities can be represented by sub-CPNs, this makes the entire CPN more manageable. (2) improve the communications between the restoration participants by sharing the restoration status on the CPN.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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