Material characterization using ultrasound tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Characterization of material properties can be performed using a wide array of methods e.g. X-ray diffraction or tensile testing. Each method leads to a limited set of material properties. This paper is interested in using ultrasound tomography to map speed of sound inside a material sample. The velocity inside the sample is directly related to its elastic properties. Recent develop-ments in ultrasound diffraction tomography have enabled velocity mapping of high velocity contrast objects using a combination of bent-ray time-of-flight tomography and diffraction tomography. In this study, ultrasound diffraction tomography was investigated using simulations in human bone phantoms. A finite element model was developed to assess the influence of the frequency, the number of transduction positions and the distance from the sample as well as to adapt the imaging algorithm. The average velocity in both regions of the bone phantoms were within 5% of the true value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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