The Effect of Varying the Density-Modulus Relationship Used to Apply Material Properties in a Finite Element Model of the Distal Ulna
Bibliographic record
Abstract
In many areas of orthopaedic biomechanics, such as implant design, properly developed Finite Element (FE) models can be a great companion to in-vitro studies, as they may allow a wider range of experimental variables to be explored in a cost-effective and timely manner. One challenge in developing these models is the assignment of accurate material properties to bone. Through the use of computed tomography (CT), many recent studies have developed subject-specific FE models, where material properties of bone are assigned based on density information derived from the scans. This involves the use of an equation to relate density and elastic modulus. There are several such relationships from which to choose in the literature. Most FE studies tend to use one of these multiple equations without justification or investigation into its appropriateness for the model.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".