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
Distribution Systems traditionally have a tree-like structure with several branches. They supply loads that vary through the day and as a result, some branches are loaded more than others are. By reconfiguring the system, loads from the overloaded branches may be moved to under-loaded branches. Consequently, loads in the branches can be balanced so that the real power losses are reduced and the voltage profile is improved. While Smart grid technologies in the future will facilitate real-time reconfiguration of distribution systems, it requires the use of efficient and fast methods. In that direction, this paper proposes a smart reconfiguration method without using load flow. It models the distribution system as a fuzzy graph using a data structure. It quantifies membership functions of edges of fuzzy graph using line impedance values and uses approximate MVA flow in the lines to characterize the fuzzy graph. Using the membership functions and line flow values, the method computes a fuzzy system participation function. The proposed method minimizes this function to balance the loads in the branches and consequently minimizes real power losses. The paper reports results from a sample system study to demonstrate the proposed method.
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