Recent advancements in the analysis of bone microstructure: new dimensions in forensic anthropology
Bibliographic record
Abstract
Bone is a mechanically active, three-dimensionally (3D) complex, and dynamic tissue that changes in structure over the human lifespan. Bone tissue exists and remodels in 3D and changes over time, introducing a fourth dimension. The products of the remodelling process, secondary and fragmentary osteons, have been studied substantially using traditional two-dimensional (2D) techniques. As a result, much has been learned regarding the biological information encrypted in the histomorphology of bone, yielding a wealth of information relating to skeletal structure and function. Three-dimensional imaging modalities, however, hold the potential to provide a much more comprehensive understanding of bone microarchitecture. The visualization and analysis of bone using high-resolution 3D imaging will improve current understandings of bone biology and have numerous applications in both biological anthropology and biomedicine. Through recent technological advancements, we can hone current anthropological applications of the analysis of bone microstructure and accelerate research into the third and fourth dimensional realms. This review will explore the methodological approaches used historically by anthropologists to assess cortical bone microstructure, spanning from histology to current ex vivo imaging modalities, discuss the growing capabilities of in vivo imaging, and conclude with an introduction of novel non-histological modalities for investigating bone quality.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".