<title>Analysis of MP3-compressed Doppler ultrasound quadrature signals</title>
Bibliographic record
Abstract
The effect of lossy, MP3 (MPEG-Layer 3) compression on clinically important Doppler parameters - derived from spectral analysis of Doppler ultrasound signals - was investigated. Ten, 10-second acquisitions of gated Doppler ultrasound signal were collected in a phantom perfused with a pulsatile flow waveform. Doppler data were collected using two sample volume lengths - 1.5 mm and 10 mm. The in- phase and quadrature Doppler signals were digitized at 44.1 kHz and compressed using four grades of signal compression (with corresponding compression ratios given in brackets): uncompressed, 128 kbits/s (11:1), 64 kbits/s (44:1). The digital audio signals were identically processed with a Fourier analysis program that provided an estimate of the instantaneous Doppler frequency (velocity) spectrum and derived parameters such as peak velocity, mean velocity, spectral width, total integrated power, and ratio of spectral power from negative and positive velocities. Analysis of variance indicated there were no significant differences (p>0.05) observed in the peak or mean velocities, spectral width, or the power ratio derived from 128 kbits/s and 64 kbits/s audio signals when compared to the uncompressed audio signals (both sample volume lengths) and the 128 kbits/s audio signals (10 mm sample volume length). However, for the 32 kbits/s audio signals, significant differences (p<0.001) were found in all of the studied parameters.
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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 | 0.002 |
| 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) | 0.005 | 0.001 |
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".