Methodological considerations for analyzing trabecular architecture: an example from the primate hand
Bibliographic record
Abstract
Micro-computed tomographic analyses of trabecular bone architecture have been used to clarify the link between positional behavior and skeletal anatomy in primates. However, there are methodological decisions associated with quantifying and comparing trabecular anatomy across taxa that vary greatly in body size and morphology that can affect characterizations of trabecular architecture, such as choice of the volume of interest (VOI) size and location. The potential effects of these decisions may be amplified in small, irregular-shaped bones of the hands and feet that have more complex external morphology and more heterogeneous trabecular structure compared to, for example, the spherical epiphysis of the femoral head. In this study we investigate the effects of changes in VOI size and location on standard trabecular parameters in two bones of the hand, the capitate and third metacarpal, in a diverse sample of nonhuman primates that vary greatly in morphology, body mass and positional behavior. Results demonstrate that changes in VOI location and, to a lesser extent, changes in VOI size had a dramatic affect on many trabecular parameters, especially trabecular connectivity and structure (rods vs. plates), degree of anisotropy, and the primary orientation of the trabeculae. Although previous research has shown that some trabecular parameters are susceptible to slight variations in methodology (e.g. VOI location, scan resolution), this study provides a quantification of these effects in hand bones of a diverse sample of primates. An a priori understanding of the inherent biases created by the choice of VOI size and particularly location is critical to robust trabecular analysis and functional interpretation, especially in small bones with complex arthroses.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".