Bone quality in prehistoric, cis‐baikal forager femora: A micro‐CT analysis of cortical canal microstructure
Bibliographic record
Abstract
Bone quality, a contributor to bone strength, is determined by structural and mechanical properties, which may be analyzed by gross and/or microscopic methods. Variables that contribute to bone quality, such as porosity, can provide insight into the health and lifestyles of people in prehistory. This study tests the ability of microcomputed tomography (µCT) to capture and characterize cortical canal systems in archaeological bone. Seven variables and 71 femora are analyzed to explore bone dynamics in prehistoric foragers from Lake Baikal, Siberia. The results indicate that canal number and canal separation differ significantly (P < 0.05) between age-at-death categories, but only for the pooled and male samples. When merged into a new variable by means of principal components analysis, canal diameter and canal surface to canal volume are also able to discriminate amongst age-at-death categories, as well as between the sexes. However, the overall lack of significant differences between the sexes and amongst age-at-death categories indicates that Baikal forager bone quality (i.e., canal architecture) did not change drastically throughout the lifespan. Interestingly, principal component one identified an untested variable that contributes to canal microstructure variability, and a sexual division of labor may promote divergent trends in canal degree of anisotropy between the sexes. Overall, µCT provides an alternate method for exploring bone quality in archaeological remains, complementing existing methods such as thin-sectioning and gross morphological analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".