Interpolation artifacts and bidimensional ensemble empirical mode decomposition
Bibliographic record
Abstract
The empirical mode decomposition (EMD) method developed by Huang et al. (1998) is a powerful signal analysis technique for non-stationary and nonlinear systems. EMD decomposes a signal into a sum of intrinsic oscillatory components, called Intrinsic Mode Functions (IMFs). Each IMF has different frequency components, potentially highlighting different geologic and stratigraphic information (Magrin-Chagnolleau & Baraniuk, 1999; Han & Van der Baan, 2011). Furthermore, high-resolution time-frequency analysis is possible by combining EMD with the instantaneous frequency. The resulting time-frequency resolution promises to be significantly higher than that obtained using traditional time-frequency analysis tools, such as short time Fourier and wavelet transforms (Han and Van der Baan, 2013). Furthermore, Bekara & Van der Baan (2009) utilize EMD in frequency-distance (f-x) domain to suppress the random and coherent noise.
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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 | 0.000 |
| 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 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".