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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.099 | 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".