Refining anticipation of degraded bone microstructures during osteoporosis based on statistical homogenized reconstruction method via quality of connection function
Bibliographic record
Abstract
Nowadays, osteoporosis disease that is related to aging has become a proliferating problem in worldwide society. It is therefore crucial to understand its evolution and predict this phenomenon precisely for different types of bone and volume fractions with adequate mathematical model. The application of statistical reconstruction method would be a helpful tool to predict osteoporosis for the simplified bone microstructures. To model osteoporosis evolution over time, in a first step, we propose to degrade the volume fraction with a mathematical model to reach any determined volume fraction between the initial condition and the degraded one with a statistical interpolation. In a second step, the degraded microstructure will be optimized using a statistical descriptor. The final optimized microstructures will be discussed as a function of the effective mechanical properties. The capability of quality of connection and two-point correlation functions (TPCFs) in 3D models and their application in the optimization of reconstructed interpolated models are going to be demonstrated. Finally, we will demonstrate and discuss the advantages of using the Quality of Connection Function (QCF) as a replacement of TPCF over the sole statistical descriptor named TPCF. We will show that QCF descriptor is better than TPCF only to find the optimized reconstructed models in a determined volume fraction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".