Criteria to select kernel functions for positive time-frequency distributions of theoretical time-varying signals
Bibliographic record
Abstract
The development of positive time frequency distributions (PTFD) is an important issue in the framework of time frequency analysis of nonperiodic signals. A PTFD can be interpreted as the signal energy localized in both time and frequency domains. A PTFD representation depends on a kernel function which is related to the signal to be analyzed. For any particular signal, it is difficult to know its PTFD by only using time and frequency marginal distributions. We show that the uncertainty coefficient based on the entropy principle is a good approach to deciding which kernel function could be appropriate for analyzing a set of sinusoidal signals. As well, a lower bound of the uncertainty coefficient was defined based on the marginal product (correlationless case of PTFD). Three known kernel functions were used to calculate the PTFD of six sinusoidal signals. These signals were considered as a simple approximation to oscillation changes in the electrical activity of the human colon. One of the kernel functions was useful for analyzing almost all sinusoidal signals.>
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".