Foetal bison long bones and mortality season estimates at the early Holocene Casper and Horner II sites, North America
Bibliographic record
Abstract
Abstract Foetal animal remains present the opportunity to investigate seasonal prey mortality in archaeological contexts. Mortality seasons may be inferred by adding a gestation age, as estimated from foetal skeletal growth curves, to a known conception period. For example, given an October conception period, the remains of a 6‐month‐old foetal animal suggest deposition in April. However, three problems complicate these investigations: (1) Conception periods in animal populations are generally not discrete windows or dates but rather distributions of events though a calendar year; (2) variation in foetal development implies that any given skeletal state corresponds to a range of gestation ages, and (3) evolutionary changes in a taxon's foetal growth may make modern growth curves poor analogues for past populations. We develop a probabilistic method to address the first two problems and partially address the third problem. The method is specific to early Holocene bison in North America (~10 000 to 8000 cal. BP), and we provide example applications: the Horner II Site, with one foetal humerus, and the Casper Site, with five foetal elements. Results for Horner II indicate that the foetal humerus is consistent with the fall or winter mortality season inferred from bison dentitions at the site, whereas results for Casper suggest a spring mortality event or events distinct from most of the site's adult bison specimens. This may reflect real seasonality variability at Casper, although methodological assumptions warrant caution for this interpretation; Future work should focus on testing these assumptions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".