Methods and theory in bone modeling drift: comparing spatial analyses of primary bone distributions in the human humerus
Bibliographic record
Abstract
This study compares two novel methods quantifying bone shaft tissue distributions, and relates observations on human humeral growth patterns for applications in anthropological and anatomical research. Microstructural variation in compact bone occurs due to developmental and mechanically adaptive circumstances that are 'recorded' by forming bone and are important for interpretations of growth, health, physical activity, adaptation, and identity in the past and present. Those interpretations hinge on a detailed understanding of the modeling process by which bones achieve their diametric shape, diaphyseal curvature, and general position relative to other elements. Bone modeling is a complex aspect of growth, potentially causing the shaft to drift transversely through formation and resorption on opposing cortices. Unfortunately, the specifics of modeling drift are largely unknown for most skeletal elements. Moreover, bone modeling has seen little quantitative methodological development compared with secondary bone processes, such as intracortical remodeling. The techniques proposed here, starburst point-count and 45° cross-polarization hand-drawn histomorphometry, permit the statistical and populational analysis of human primary tissue distributions and provide similar results despite being suitable for different applications. This analysis of a pooled archaeological and modern skeletal sample confirms the importance of extreme asymmetry in bone modeling as a major determinant of microstructural variation in diaphyses. Specifically, humeral drift is posteromedial in the human humerus, accompanied by a significant rotational trend. In general, results encourage the usage of endocortical primary bone distributions as an indicator and summary of bone modeling drift, enabling quantitative analysis by direction and proportion in other elements and populations.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".