Improvement of the Precision of Ultrasonic Microscope for Biological Tissue Using the Automatic Extraction of the Reference Signals
Bibliographic record
Abstract
This paper deals with the precision of the ultrasonic microscopy for biological tissue characterization. The estimation error of sound speed influenced by the selection of reference points for obtaining the reference signal is described. In well known scanning type microscope, a focused ultrasonic signal is irradiated to the glass substrate on which a thin sliced tissue is attached. The reflected signal from the front surface of the tissue is compared with the signal from the glass substrate on which no tissue is attached. As the scanning plane and glass substrate are not completely in parallel, it is necessary to locate the glass surface at each measuring point. For this purpose, several reference points on which no tissue is attached are selected in the view field. The equation of the plane representing the glass surface is consequently obtained using the reflection at these reference points. However, the glass surface has an apparent surface roughness. This brings an estimation error of the equation of the plane. This error can be reduced by increasing the number of reference points. In order to extract a sufficient number of reference points automatically, an algorithm using the reflected wave was proposed to discriminate the points where the glass surface is exposed. Although the estimation error of sound speed with three reference points was as large as 31 m/s (as the double of standard deviation), it was reduced into as small as 16 m/s, when 1000 reference points had been automatically extracted.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".