Techniques for field application of lingual ultrasound imaging
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Bibliographic record
Abstract
Techniques are discussed for using ultrasound for lingual imaging in field-related applications. The greatest challenges we have faced distinguishing the field setting from the laboratory setting are the lack of controlled head/transducer movement, and the related issue of tissue compression. Two experiments are reported. First, a pilot study identifies important factors in controlling head/transducer movement in field settings. Second, an Optotrak/ultrasound study reports the range of head movement in an optimal field-like setting within and across varying phonetic contexts, as well as the effect of tongue tissue compression on tongue image data. Results suggest that with a simple arrangement involving a head rest or surface, a fixed transducer, and careful design and presentation of stimuli, reliable lingual ultrasound data can be collected in the field.
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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 | 0.017 |
| 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 | 0.001 | 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 it