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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".