Paleopathology, Entheseal Changes, and Cross-Sectional Geometry: The Zooarchaeology of Working Animals
Bibliographic record
Abstract
Morphological changes in the skeletons of working animals such as reindeer, horse, and cattle have long been observed and documented in the archaeological record. Activities such as riding, carrying cargo on their backs, and pulling vehicles like sleds and ploughs throughout an animal’s life history cause alterations and variations to skeletal tissue. Such alterations include paleopathological lesions, entheseal changes (EC)—alterations in muscle, tendon, and ligament attachment sites on bone—and variations in cross-sectional bone geometry (CSBG). These clues are helpful for reconstructing human-animal relationships in faunal remains of our archaeological past. However, other factors influence the morphological appearance of skeletal tissue besides working activities, such as age, sex, body size, nutrition, genetics, environmental factors, and management by human caretakers. This article explores how paleopathological lesions, EC, and CSBG in faunal skeletal remains are examined to reconstruct working activity and changes to human-animal relationships in the archaeological record. In particular, we discuss two primary topics of inquiry: (1) a review of paleopathological identifiers in working animals such as cattle, horse, camel, and reindeer; and (2) how EC and CSBG are understood in terms of bone functional adaptation, and their application in working and non-working animals such as reindeer and horse. Next, we analyze each topic highlighting their benefits and limitations, including how they contribute to archeological understandings of human-animal relationships in the past, as well as their implications for future research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".