Bibliographic record
Abstract
Spatially variant sidelobe apodization (SVA) is a technique that reduces sidelobe levels in a Fourier image while maintaining the image resolution that would be obtained using the rectangular window. The method has been successfully used in inverse synthetic aperture radar (ISAR) imaging. In general, sidelobe apodization techniques can be used whenever a finite amount of data is processed by a discrete-time Fourier transform. Thus, the technique can be extended to magnetic resonance imaging (MRI) and time-frequency analysis. We explore the impact of sidelobe apodization on these Fourier imaging techniques. The characteristics of the data processed in these applications present singular challenges. In one instance the data consist of reflectivity samples from high intensity airborne scattering structures. In another case the data consist of signals with high signal-to-noise ratios in which intensities decay with time. A third instance is that of data consisting of nonstationary and transitory signals. Advantages and disadvantages of sidelobe apodization are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".