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Record W2804665911 · doi:10.1002/oa.2677

Raw material preferences for scapular tools: Evaluating water buffalo age bias in the early<scp>H</scp>emudu culture,<scp>C</scp>hina

2018· article· en· W2804665911 on OpenAlexaff
Liye Xie, Mary C. Stiner

Bibliographic record

VenueInternational Journal of Osteoarchaeology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScapulaUngulateRaw materialBiologyGeographyAnatomyEcology

Abstract

fetched live from OpenAlex

Abstract Our research on scapular earth‐working implements from the early Hemudu culture (7000–6000 bp ) 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.294
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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