Bibliographic record
Abstract
Sonar systems using Linear Frequency Modulated (LFM) Continuous Active Sonar (CAS) waveforms can provide a higher target update rate than conventional Pulsed Active Sonar (PAS) waveforms through sub-pulse processing. The higher target update rate promises to improve tracking performance. The drawback of sub-pulse processing is that reducing the duration and bandwidth of the processed waveform decreases the Signal-to-Noise Ratio (SNR) or Signal-to-Reverberation Ratio (SRR) of the target echoes. Previous work suggests that incoherently averaging CAS sub-pulses can exploit the range-Doppler bias to make an estimate of the target Doppler as well as improve overall detection performance. The drawback of this technique is that the higher target update rate of the CAS waveform is not exploited for tracking. Here a two pass approach is presented that both retains the higher target update rate while simultaneously removing the range-Doppler bias. Results using this two-pass approach will be shown using the Littoral Continuous Active Sonar 2016 sea trial (LCAS16).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".