Precision requirements for specifying transmitter waveforms used for modelling the off-time electromagnetic response
Bibliographic record
Abstract
In order to obtain an accurate EM response with modelling software, most people assume that it is necessary to know or specify the excitation current waveform (or its derivative) precisely. A mathematical analysis shows that accurate model results can be obtained during the off time if the amplitude of the waveform is specified precisely in the latter parts of the waveform; however, in the earlier parts of the waveform, the amplitudes can be approximate as long as the area under the waveform is specified accurately. This means that the discretization should be fine in the latter parts of the waveform, but can be coarse in the early parts of the waveform. Coarse sampling of the waveform means that the convolution integrals can be calculated more efficiently. An example shows that the exponential rise and linear ramp assumed by some modelling software to approximate a waveform can give poor results with errors close to 10%. Another approximate waveform that is precise in the final parts of the waveform and has an accurate area under the waveform curve gives errors less than 0.15%.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".