Automatic Detection and Frequency Estimation of Oscillatory Variables in the Presence of Multiple Oscillations
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
Automatic detection of oscillatory variables in the presence of multiple oscillations is still a challenging problem in the literature, despite the fact that there are several methods for detection and estimation of single-frequency oscillation. A method is proposed that utilizes the autocorrelation function (ACF) to detect the oscillatory variables and estimate the oscillation periods in the presence of multiple oscillations. The advantage of the developed method is that it requires no or little human interference in the detection process. It is also capable of estimation of the decay rate for decaying oscillations and is advantageous over methods based on analyzing the power spectrum for oscillation detection in case of nonsinusoidal oscillations. The proposed method is verified through a case study.
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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 | 0.001 | 0.003 |
| 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