Raw material preferences for scapular tools: Evaluating water buffalo age bias in the early<scp>H</scp>emudu culture,<scp>C</scp>hina
Bibliographic record
Abstract
Abstract Our research on scapular earth‐working implements from the early Hemudu culture (7000–6000bp) in China reveals prehistoric raw material selection in bone tool manufacture, specifically in regard to animal age. Although the scapula normally would not be considered for distinguishing among adult age classes, the maturity and thickness of the bone were an important consideration for its technological performance. Statistical analysis of the width and rugosity level of the scapular neck show that scapulae from older adult wild buffalo was strongly preferred for crafting earth‐working implements. This raw material was less commonly available to tool makers in comparison with the bones of younger animals. In order to conserve the scapulae of old buffalo for tool production, the Hemudu people likely took these heavy bones from kill sites. They seldom transported scapulae from younger animals; such raw material was rarely used, and presumably only when the desirable material was unavailable. Our approaches for reconstructing buffalo mortality patterns are based on the development of the scapular bone. In addition to water buffalo and cattle, the approach is applicable to other ungulate scapulae that have been transformed into artefacts (tools and oracle bones) in prehistoric and historic cultures. This attempt at testing for age bias in scapular raw material selection can further stimulate methodological development to reveal activities surrounding bone acquisition in preindustrial societies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".