Proper orthogonal projection - multiple signal classification (POP-MUSIC)
Bibliographic record
Abstract
Proper Orthogonal Projection - Multiple signal classification (POP-MUSIC) is introduced. Two spectral estimates are derived using geometrical approach based on POP-MUSIC which appear to be the counterparts of the Maximum Likelihood (ML) spectral estimate and Linear Prediction (LP) spectral estimate. POP-MUSIC based on linear prediction (POP-MUSIC-LP) provides a superior resolution compared to POP-MUSIC based on maximum likelihood (POP-MUSIC-ML), and this is justified mathematically. Quantitative measures such as degrading factor due to spurious peaks (F) and sharpness factor (Y) are introduced to facilitate the comparative performance of the spectral estimates. A systolic array structure which is suitable for VLSI implementation is given for adaptive estimation of the cross spectral density matrix and the POP-MUSIC spectral estimation. Computer simulation is presented.
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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 | 0.001 | 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 | 0.001 |
| 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".