Technical note: investigating activity-induced 3d hand entheseal variation in a documented South African sample
Bibliographic record
Abstract
Abstract For reconstructing physical activity in the past, the surfaces of bones where muscles and ligaments attach, “entheses,” are routinely studied. Previous research has introduced an experimentally validated virtual approach for reconstructing habitual activity based on entheses. The present study relies on this virtual method to further investigate the effects of various biological factors on entheses, including variation by ancestry. Our skeletal sample includes 39 individuals from the well-preserved Pretoria Bone Collection in South Africa. Although the size of the sample is limited, all selected individuals present excellently preserved left- and right-hand bones. Moreover, all individuals are reliably documented for sex, biological age, and ancestry (i.e., African or European origin). Multivariate analyses were run on both raw and size-adjusted hand entheseal three-dimensional measurements. Our findings showed that, after size adjustment, entheseal multivariate patterns did not significantly vary by sex, biological age, or estimated body mass. However, a significant (p-value = 0.01) variation was found between individuals of different ancestries in only the right-hand side of our South African skeletal sample. The observed entheseal patterns were consistent with the habitual performance of power grasping in individuals of African origin, while our small sample’s European individuals showed distinctive indications of precision grasping behaviors. This pilot research provided important new insights into potentially activity-induced differences between population samples from South Africa, supporting the value of the applied protocol in reconstructing aspects of past human lifestyles. In the future, the functional interpretations of this study on interpopulation variation may be validated using increased sample sizes and individuals with long-term occupational documentation.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".