Bibliographic record
Abstract
Several high-frequency ultrasound techniques have been developed during the last decade with the intention of assessing elastic properties of bone at the tissue level. The basic measurement principles can be divided into: 1) measurement of the compressional wave velocity in thin tissue sections; 2) measurement of surface acoustic wave velocities in thick sections; and 3) derivation of the acoustic impedance from the confocal reflection amplitude in thick sections. In this paper, the 3 principles are described with example measurements given in the frequency range from 50 MHz to 1.2 GHz. The measurements were made with 2 microscopes operating in the pulse-echo mode, either with frequencies up to 200 MHz and time-resolved detection or between 100 MHz and 2 GHz and amplitude detection. The methods are compared and their application potentials and limitations are discussed with respect to the hierarchical structure of cortical bone. Mapping of the confocal reflection amplitude has superior capabilities for deriving quantitative elastic and structural parameters in the heterogeneous bone material. Even at low frequencies (50 MHz), the mineralized tissue matrix can be separated from the larger pores (Haversian canals), and the elastic coefficient in the probing direction can be measured in 2 dimensions. Depending on the type of sample surface preparation (flat or cylindrically shaped), local distribution of a single elastic coefficient or the average transverse isotropic stiffness tensor can be derived. With frequencies in the GHz range, the lamellar bone structure can be analyzed. However, at one GHz, the acoustic wavelength is still one order of magnitude larger than the individual mineralized collagen fibrils. Although the thickness of a lamellar unit can easily be assessed from the acoustic image, the derivation of the anisotropic elastic properties of the mineralized collagen fibrils as well as the detailed structure of a lamella can only be accomplished with further model assumptions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".