Heuristic Determination of Distribution Trees
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
Electrical distribution networks develop configurations that deviate from the original long-term plan. The Distribution Trees Problem (DTP) is one means of measuring this development, which finds the deviation between long-term planning and the optimal topology for the actual conditions of the network. Each feasible solution corresponds to a set of directed out-trees rooted at the substations. DTP takes into account characteristics of the substations and consumer demand. It also determines the optimal topology of the network to distribute electrical energy at minimum cost. In this paper, we use two search techniques to solve this problem: 1) simulated annealing and 2) tabu search. Nine different problems within 500 to 30 000 consumer points and 20 substations were used to calibrate the parameters of both methods and to compare their efficiency. The numerical results indicate that the efficiency of simulated annealing decreases as the problem size increases, and that tabu search is more efficient than simulated annealing.
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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 | 0.000 | 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