Entheseal Changes: Benefits, Limitations and Applications in Bioarchaeology
Bibliographic record
Abstract
Reconstructing physical activities in ancient humans has long been pursued in bioarchaeology to understand our history and development. Entheseal changes (EC)––variations to muscle, tendon, and ligament attachment sites on bone––have been used in bioarchaeology since the 1980s to reconstruct activities in past populations such as changes in mobility, subsistence strategy, and gendered division of labour. EC research is based on bone functional adaptation, where bone responds to mechanical stress on entheses through bone formation or destruction in varying degrees of expression. However, the relationship between EC and activity is more complex than simple cause-and-effect, as it involves multiple confounding variables, which can affect EC morphology. This article addresses the use of EC research in bioarchaeology through two parts: Part 1 defines entheses and EC, including observational and quantitative methods developed in bioarchaeology to study EC. Part 2 will summarize the main known factors that influence EC beyond activity such as age, sex, and body size. The article concludes with a discussion of varying benefits and limitations to EC research in bioarchaeology including the use of archaeological samples, historical collections, and animal experimental models. Overall, EC research can be difficult to link with activity due to its multifactorial etiology, challenges of efficacy in developing methods, and limitations of working with human remains. However, recent studies are showing more positive results, demonstrating the usefulness of EC as a way to reconstruct activity.
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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".