Using point clouds to investigate the relationship between trabecular bone phenotype and behavior: An example utilizing the human calcaneus
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to demonstrate a new method for analyzing trabecular bone volume fraction and degree of anisotropy in three dimensions. METHODS: We use a combination of automatic mesh registration, point-cloud correspondence registration, and P-value corrected univariate statistical tests to compare bone volume fraction and degree of anisotropy on a point by point basis across the entire calcaneus of two human groups with different subsistence strategies. RESULTS: We found that the patterns of high and low bone volume fraction and degree of anisotropy distribution between the Black Earth (hunter-gatherers) and Norris Farms (mixed-strategy agriculturalists) are very similar, but differ in magnitude. The hunter-gatherers exhibit higher levels of bone volume fraction and less anisotropic trabecular bone organization. Additionally, patterns of bone volume fraction and degree of anisotropy in the calcaneus correspond well with biomechanical expectations of relative forces experienced during walking and running. CONCLUSIONS: We conclude that comparing site-specific, localized differences in trabecular bone variables such as bone volume fraction and degree of anisotropy in three-dimensions is a powerful analytical tool. This method makes it possible to determine where similarities and differences between groups are located within the whole skeletal element of interest. The visualization of multiple variables also provides a way for researchers to see how the trabecular bone variables interact within the morphology, and allows for a more nuanced understanding of how they relate to one another and the broader mechanical environment.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".