Closure to discussion of "z-bus loss allocation"
Bibliographic record
Abstract
km . Thus, all lines no matter how short have both susceptance and reactance. The susceptances of a realistic transmission network statistically described below in item 9) clearly show that these shunt elements are nonzero. 2) The discussers also bring up “other cases” where property (g) may not be satisfied, however they do not provide much detail to justify this claim. In one case, with very small shunt susceptances, it is claimed that the real part of -bus matrix is very sensitive to transformer resistances. However, when we analyzed their five-bus test network with and without transformer resistances, the differences in both the real and imaginary parts of the -bus matrix were very small. 3) The authors provided simulation results for five cases on their test network. For each case, the load flow and the corresponding loss allocation factors, , were calculated. We successfully reproduced two cases: 1) base case and 2) case with and . However our results differed substantially for the other three cases in the system losses computed. In the three discrepant cases, our load flows, run using PowerWorld 7.0 (http://www.powerworld.com), gave substantially lower system losses and, therefore, different loss allocation factors. Nevertheless, in the next point we discuss the two cases in common, to which we add the results we obtained for one of the discrepant cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".