M-Model: A New Precise Medium-Length Transmission Line Model
Bibliographic record
Abstract
Real transmission lines are translated into mathematical models using either the lumped parameter approach or the distributed parameter approach. The first one is used for short- and medium-length transmission lines, while the other is used for long-length transmission lines where the accuracy and precision are required. For medium transmission lines, the lumped parameter approach can be applied using one of four popular circuit representations known as gamma ( Γ), opposite-gamma , tee (T), and pi Π. This study presents a new circuit representation called em (M). This model is inspired by the sagging phenomenon where, at the sag point, the distributed series impedance of the Π-model is divided into two equal/unequal parts and the distributed shunt admittance at the center is bigger than that at both ends. For some numerical experiments, the M-model shows a stunning performance in estimating transmission line readings. It wins in most cases and, for the few remaining cases, the M-model shows very competitive results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".