Nonstationary predictive deconvolution based on a partition of unity
Bibliographic record
Abstract
A partition of unity (POU) is a discrete set of usually overlapping windows that sum exactly to one for a finite interval on the real line. Multiplying a signal by the POU decomposes it into a set of temporally localized signals or Gabor slices. Applying any stationary operator to these slices and allowing the operator to depend upon the slice defines a nonstationary operator. We apply a stationary prediction operator to each slice and by summing construct a time-domain nonstationary deconvolution method based on gapped prediction filtering. We call this new method slicedecon because it operates directly on the individual Gabor slices. We also prescribe the construction of nonstationary autocorrelation functions as an analysis tool. We then compare slicedecon with the more established Gabor deconvolution or gabordecon. When the prediction filtering is unit-lag, we show that slicedecon achieves results comparable to gabordecon on a nonstationary (Q attenuation) synthetic. For lags greater than unity slicedecon appears to suppress, though not eliminate, periodicities in the nonstationary autocorrelation of a signal. Testing on a synthetic with multiples has not yet indicated any dramatic elimination of the unwanted multiple reflections.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".