Derivation of elastic stiffness from site-matched mineral density and acoustic impedance maps
Bibliographic record
Abstract
200 MHz acoustic impedance maps and site-matched synchrotron radiation micro computed tomography (SR-muCT) maps of tissue degree of mineralization of bone (DMB) were used to derive the elastic coefficient c(33) in cross sections of human cortical bone. To accomplish this goal, a model was developed to relate the DMB accessible with SR-muCT to mass density. The formulation incorporates the volume fractions and densities of the major bone tissue components (collagen, mineral and water), and accounts for tissue porosity. We found that the mass density can be well modelled by a second-order polynomial fit to DMB (R(2) = 0.999) and appears to be consistent with measurements of many different types of mineralized tissues. The derived elastic coefficient c(33) correlated more strongly with the acoustic impedance (R(2) = 0.996) than with mass density (R(2) = 0.310). This finding suggests that estimates of c(33) made from measurements of the acoustic impedance are more reliable than those made from density measurements. Mass density and elastic coefficient were in the range between 1.66 and 2.00 g cm(-3) and 14.8 and 75.4 GPa, respectively. Although SAM inspection is limited to the evaluation of carefully prepared sample surfaces, it provides a two-dimensional quantitative estimate of elastic tissue properties at the tissue level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".