Supraharmonics Assessment Methods: Variability Versus Computational Resources
Bibliographic record
Abstract
This article digs into the numeric uncertainty for supraharmonic assessment methods and proposes a robust reference for calibration. A detailed resource survey of candidate methods, digital Special International Committee on Radio Interference (D-CISPR) and light-quasi-peak (QP), shows that computing resources should not be the primary concern, considering both necessitates an acceptable level of resources and requires the same mid-range field-programmable gate array (FPGA). Furthermore, a new criterion for selecting and optimizing numerical methods is proposed: the variability caused by time-shifting operations. D-CISPR, a proposed D-CISPR variation, the light-QP, and the proposed numerical-heterodyne methods were studied with the CISPR 16 impulse train test and three examples of real-world signals. Results demonstrate that the variability concerning time shift is less than 1% for the D-CISPR-based methods but up to 50% for the light-QP method.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".