A novel high resolution parallel spectral estimation method for narrow-band signals
Bibliographic record
Abstract
A high resolution parallel algorithm is proposed for estimating the spectrum of a narrow-band signal from a short data record. The algorithm is based on combining the nonparametric and parametric approaches, where the nonparametric approach is used to decompose the measurement data into an orthogonal set of components, and the parametric approach is used to estimate the model of these components in parallel. A fast Fourier transform (FFT) is used to decompose the signal. A singular value decomposition (SVD)-based linear predictive coding algorithm (LPCA) is used to obtain an autoregressive moving average (ARMA) model of the signal components. The FFT of the signal components is translated to the low-frequency region, and their inverse FFTs are decimated before estimating the ARMA model so as to separate the closely-spaced modes. The spectra of the estimates are translated back to their original location. The proposed algorithm is evaluated using simulation.>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".