Spatial prediction filtering in fractional Fourier domains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We generalize the time and frequency spatial predictive filtering techniques by means of the fractional Fourier transform. This time‐frequency transform is a generalization of the Fourier transform by introducing a fractional parameter that allows transformation to any of a continuous family of spaces intermediate to the time and frequency domains. The family of fractional Fourier transforms of a signal can be considered as interpolated representations between the signal and its Fourier transform. Prediction techniques, such as spatial prediction filtering, are based on the assumption that the signal to be filtered is composed of two parts: one predictable, the coherent signal and other unpredictable, the random noise. A lateral prediction algorithm estimates the predictable component of a trace from its neighboring traces. In the conventional spatial prediction process, lateral coordinates are always spatial and the vertical coordinate can be either time or frequency. By applying the fractional Fourier transform in the vertical direction we extend the prediction techniques to a continuum of mixed time‐frequency domains in which time and frequency are just particular cases. We test the method in the new domains using stationary a non‐stationary synthetic seismic data.
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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) | 0.001 | 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 it