On Dynamic Evaluation of Harmonics Using Generalized Averaging Techniques
Bibliographic record
Abstract
Two distinct methods of applying generalized averaging techniques appear in the literature. The first method involves a direct mathematical mapping of all system equations and inputs into the frequency domain to produce harmonic coefficients that precisely match those produced by a sliding window Fourier decomposition during transient events. Although this approach reproduces the same harmonic coefficients as a sliding window decomposition, it is shown that reconstruction of the time domain waveforms does not match the response of the underlying time domain system during transients. In contrast, a second method has recently been employed by some researchers that does not strictly map all system equations and inputs into the frequency domain, and yet it has been shown to precisely reproduce time domain results during transients. This paper shows that this matching of the time domain transients comes at the expense of introducing spurious dynamics into the associated harmonic domain model, which may not exist in the underlying time domain system. The limitations and implicit assumptions behind these two methods are identified and compared, thus allowing researchers to readily determine which compromises are to be made based on the application in question.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".