Detection of Non Random Phase Signal in Additive Noise with Surrogate Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The Surrogate Analysis (SA) is known to detect nonlinear signals, non-stationary signals and ARMA systems driven by non-Gaussian processes. This paper adds to address the detection of non-random phase signal, of which the linear phase signal is the best-known example. This is a new interpretation of the SA. In order to highlights the benefits of the interpretation, a new theoretical signals is constructed. The signal has a perfect Gaussian distribution and is not affected by periodic extension and is a linear phase signal. The SA will be shown able to detect this signal in a noise with exactly the same power spectrum. It will be clear that the SA is able to detect phase linearity even when the data is normally distributed. An application of the detection by SA is given regarding very noisy and short time electrocardiogram (ECG) signal and compared to higher order statistics and normality tests for this purpose.
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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 | 0.001 |
| 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) | 0.000 | 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