Frequency‐domain Characterization of Signals and Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The treatment of biomedical signals as stochastic processes provides flexibility and a sense of generality in analysis, but imposes conditions and requirements in the estimation of their statistics including the ACF and power spectral density (PSD). In this chapter, the authors investigate methods to estimate the PSD and frequency-domain parameters of biomedical signals and systems. They also study methods to derive spectral parameters that can characterize the given signal as well as the system that generated the signal. The motivation for the study, as always, will be to distinguish between normal and abnormal signals or systems, and the potential use of the methods in diagnosis. The problem statement is generic and represents the theme of the present chapter. The various signal analysis techniques described and the examples used for illustration will address the points raised in the problem statement, with attention to specific problems and techniques.
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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 | 0.000 |
| 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 it