Statistically optimal null filters for processing short record length signals
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Bibliographic record
Abstract
In this paper, we propose an alternate non-parametric statistically optimal method of null filtering. One of the important features of this method lies in its ability to process signals of short record lengths. The optimality criteria for maximum output SNR and the minimum mean-square error are combined to generate the new approach. The method is first designed for the coherent case (where the desired signal shape is a priori known) and later extended to include the non-coherent case based on orthogonal signal expansion. To deal with a non-orthogonal signal expansion, we propose a sliding Gram-Schmidt orthogonalization. An application to separate two closely spaced damped sinusoids is considered. Simulation results are presented comparing the proposed methods with the conventional one based on Constrained Notch Filtering.
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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 | 0.000 | 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 it