Exploring pixel difference noise floor in tongue ultrasound data
Bibliographic record
Abstract
Pixel Difference (PD) is a change metric for image sequences used to detect tongue movement in ultrasound data. It measures overall change by treating each frame as a vector and calculating vector norms between consecutive images. The PD curves have a substantial noise floor but it is unclear to what extent this noise is caused by physiological processes like the pulse and tensing and relaxation of muscle fibres. To evaluate the contribution of physiological processes to the PD noise floor, we measured one human participant at rest and an excised bovine tongue using the same ultrasound imaging parameters. The mean PD ± standard deviation for human tongue was 93,851 ± 8409 in rest and 274,348 ± 207,791 in movement. For the bovine tongue we obtained five different locations between the root and the tip. The mean PD range was (109,156, 176,433) and standard deviation range was (986, 1273). In the bovine samples, mean PD correlates with standard deviation (r = 0.9972) in line with the multiplicative noise model. The data from human at rest differ from this pattern due to a higher standard deviation. This suggests that the PD standard deviation—not the PD noise floor—reflects physiological processes in resting tongue ultrasound data.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".