Raman Spectroscopy-based Analysis of Cartilage Composition with Applications in Finite Element Modeling
Bibliographic record
Abstract
Articular cartilage possesses unique material properties due to a complex depth-dependent composition of sub-components.Raman spectroscopy has proven valuable in quantifying this composition through cartilage cross-sections.However, cross-sectioning requires tissue destruction and is not practical insitu.In this thesis, Raman spectroscopy-based multivariate curve resolution was employed in porcine cartilage samples (n = 12) to measure collagen II, glycosaminoglycan, and water distributions throughthe-surface and in cross-sections.These data were then used to create depth-dependent material property finite element models of cartilage, optimized to match experimental results.Through-thesurface Raman measurements could predict composition distributions up to a depth of approximately 0.5 mm.Depth-dependent FE models averaged an 18% reduction in error for predicted reaction force compared to simplified homogeneous distribution models.Use of a fructose-based optical clearing agent was found ineffective in homogenizing scattering.This measurement technique could be applicable for non-destructively modeling the evolution of joint diseases such as osteoarthritis. 2.3.6MCR composition determination ........
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".