Bibliographic record
Abstract
This chapter is concerned with beamforming in the time domain, which has the advantage of being more intuitive than beamforming in the frequency domain. Furthermore, the depicted approach is broadband in nature. The chapter describes the time-domain signal model and explains how broadband beamforming works. Then, it defines many performance measures that are essential for the derivation and analysis of broadband beamformers. Some measures are only relevant for fixed beamforming while others are more relevant for adaptive beamforming. Most of the adaptive beamformers are easily derived from the time-domain MSE criterion. The chapter also gives some important examples. It further shows how to derive the most conventional time-domain fixed beamformers from the white noise gain (WNG) and the directivity factor (DF). Finally, it also shows in great detail how to derive three classes of time-domain beamformers: fixed, adaptive, and differential.
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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 | 0.000 | 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 | 0.002 | 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".