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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".