Analysis of Complex Linear Periodically Time-Varying Circuits by Method of Reduced Matrix D-Trees
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Bibliographic record
Abstract
Abstract The paper proposes the method of reduced matrix D-trees, which is an improved version of the method of matrix d-trees. This method is a further development of the application of one of the subcircuit methods, the so-called d-tree method, to the symbolic analysis of linear circuits with constant parameters The method of reduced matrix D-trees, like the d-tree method, provides a significant reduction in the required computer time for modeling circuits, which has a mathematical meaning, consisting in the bringing out of similar terms in the formed complex symbolic expressions. Since there are, in fact, many symbolic terms in such expressions, this reduction in time is due to such factoring. The method is illustrated using a model of a long line consisting of a cascade connection of a large number of elementary links. The results of the computer simulation are also presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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