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Record W2131237347 · doi:10.1017/s0003598x00049851

The origins and spread of stock-keeping: the role of cultural and environmental influences on early Neolithic animal exploitation in Europe

2013· article· en· W2131237347 on OpenAlexaff
Katie Manning, Sean S. Downey, Sue Colledge, James Conolly, Barbara Stopp, Keith Dobney, Stephen Shennan

Bibliographic record

VenueAntiquity · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent University
FundersNatural Environment Research CouncilSight Research UK
KeywordsGeographyStock (firearms)Mediterranean climateAnimal speciesDistribution (mathematics)DomesticationArchaeologyEthnologyEcologyHistoryZoologyBiology

Abstract

fetched live from OpenAlex

It has long been recognised that the proportions of Neolithic domestic animal species—cattle, pig and sheep/goat—vary from region to region, but it has hitherto been unclear how much this variability is related to cultural practices or to environmental constraints. This study uses hundreds of faunal assemblages from across Neolithic Europe to reveal the distribution of animal use between north and south, east and west. The remarkable results present us with a geography of Neolithic animal society—from the rabbit-loving Mediterranean to the beef-eaters of the north and west. They also demonstrate that the choices made by early Neolithic herders were largely determined by their environments. Cultural links appear to have played only a minor role in the species composition of early Neolithic animal 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.220
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations70
Published2013
Admission routes1
Has abstractyes

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