Systematic pole-zero sorting method for neuro-TF modeling of electromagnetic response
Bibliographic record
Abstract
Neuro-transfer functions (neuro-TF) modeling method has been developed as one of the popular methods for parametric modeling of electromagnetic (EM) filter responses. The discontinuity issue of zero and pole data caused by extraction using vector fitting w.r.t. geometrical parameters change affects the neuro-TF training process and limits its modeling accuracy. This issue is addressed by this paper which proposes a novel systematic pole-zero sorting method for neuro-TF parametric modeling. The proposed method can obtain continuous pole-zero data which change much more smooth w.r.t. geometrical parameters change than the existing neuro-TF method, especially solves the difficulty of disorder of positive and negative values due to small values. The proposed systematic sorting method can substantially improve the modeling accuracy during the establishment and training of neuro-TF model over the existing neuro-TF method without systematic sorting.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Engineering paper on a pole-zero sorting method for electromagnetic response modeling; the object is a modeling technique in electronics.
The paper develops a modeling method for electromagnetic filter responses, not research practice.
Engineering parametric modeling of EM filter responses, not research about research.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".