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Record W2986417215 · doi:10.1371/journal.pone.0224241

Radiocarbon dating and cultural dynamics across Mongolia’s early pastoral transition

2019· article· en· W2986417215 on OpenAlexaff
William Taylor, Shevan Wilkin, Joshua Wright, Michael Dee, Erdene Myagmar, Julia Clark, Tumurbaatar Tuvshinjargal, Jamsranjav Bayarsaikhan, William W. Fitzhugh, Nicole Boivin

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Calgary
FundersMax-Planck-Institut für MenschheitsgeschichteRijksuniversiteit GroningenNational University of Mongolia
KeywordsRadiocarbon datingPrehistoryHerdingBronze AgeArchaeologyGeographyHistory

Abstract

fetched live from OpenAlex

The emergence of mobile herding lifeways in Mongolia and eastern Eurasia was one of the most crucial economic and cultural transitions in human prehistory. Understanding the process by which this played out, however, has been impeded by the absence of a precise chronological framework for the prehistoric era in Mongolia. One rare source of empirically dateable material useful for understanding eastern Eurasia's pastoral tradition comes from the stone burial mounds and monumental constructions that began to appear across the landscape of Mongolia and adjacent regions during the Bronze Age (ca. 3000-700 BCE). Here, along with presenting 28 new radiocarbon dates from Mongolia's earliest pastoral monumental burials, we synthesise, critically analyse, and model existing dates to present the first precision Bayesian radiocarbon model for the emergence and geographic spread of Bronze Age monument and burial forms. Model results demonstrate a cultural succession between ambiguously dated Afanasievo, Chemurchek, and Munkhkhairkhan traditions. Geographic patterning reveals the existence of important cultural frontiers during the second millennium BCE. This work demonstrates the utility of a Bayesian approach for investigating prehistoric cultural dynamics during the emergence of pastoral economies.

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)0.000
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 score0.016
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.188
Teacher spread0.170 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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