A comparison amongst sub-optimal ordering schemes for power systems accompanied with a GA-based optimal ordering method
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Bibliographic record
Abstract
This paper presents a review over famous methods of solving sparse linear equations and a comparison amongst the most famous sub-optimal ordering schemes in order to speed up the calculations needed for power systems analysis. A GA-based algorithm is also proposed to verify the efficiency of the existing methods whose goal is to reach the optimal reordering to minimize the number of fill-ins, the number of added nonzero elements created during so called elimination process. A remarkable comparison is, then, made between different reordering schemes using several IEEE standard power networks based on number of fill-ins, and calculation time for DC load flow, fast decoupled load flow, and LU factorization. Approximate minimum degree, for the first time, is used in power systems analysis, and has been shown to be the best method amongst the others.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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