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Record W3173345942 · doi:10.1002/oa.3020

Complexity of agricultural economies in the Yiluo region in the late Neolithic and bronze age (3500–221 BC): An integrated stable isotope and archeobotanical study from the Tumen site, North China

2021· article· en· W3173345942 on OpenAlexaff
Dawei Tao, Fei Liu, Guang Yu Ren, Michael P. Richards, Guowen Zhang

Bibliographic record

VenueInternational Journal of Osteoarchaeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsSimon Fraser University
FundersNational Social Science Fund of China
KeywordsBronze AgeAgricultureGeographyChinaSocial complexityPeriod (music)ChronologyArchaeologyLandformBronzeCartography

Abstract

fetched live from OpenAlex

Abstract The region of Yiluo in North China witnessed the development of social complexity in the Neolithic period and the emergence of the first Chinese state society in the early Bronze Age. However, the agricultural economies in the Neolithic–Bronze Age periods of this region and its influence on the social complexity in this area have not been adequately understood, owing to the relative lack of evidence from archeobotany, chronology, and stable isotope analysis in this region. Archeobotanical and isotopic evidence, together with chronological data from the Tumen site, reveals that the complexity of the agricultural economy composed of millet and rice appeared during the late Yangshao period in the Yiluo region and became more intensified in the following Longshan and Bronze Age periods. During the Yangshao and Longshan periods, environmental factors such as landform and hydrology were likely important factors for the diverse agricultural patterns. The complexity of the agricultural economy has an influence on settlement hierarchy and population growth, suggesting that the rise and intensification of the complex agricultural economy was an important driving force in societal development in the Yiluo region.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.171
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of OsteoarchaeologySame topicArchaeology and ancient environmental studiesFrench-language works237,207