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Record W2614862221 · doi:10.1038/s41598-017-02636-w

A Second Mortuary Hiatus on Lake Baikal in Siberia and the Arrival of Small-Scale Pastoralism

2017· article· en· W2614862221 on OpenAlexaff
Robert J. Losey, Andrea L. Waters‐Rist, Tatiana Nomokonova, Artur Kharinskii

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Alberta
FundersGerda Henkel Foundation
KeywordsRadiocarbon datingPastoralismArchaeologyBronze AgeFaunaHoloceneShorePeriod (music)GeographyForagingHiatusZooarchaeologyEcologyBiologyLivestockPaleontologyFishery

Abstract

fetched live from OpenAlex

The spread of pastoralism in Asia is poorly understood, including how such processes affected northern forager populations. Lake Baikal's western shore has a rich Holocene archaeological record that tracks these processes. The Early Bronze Age here is evidenced by numerous forager burials. The Early Iron Age (EIA) is thought to mark the arrival of pastoralists, but archaeological remains from this period have received little analysis. New radiocarbon dates for EIA human remains from 23 cemeteries indicate that no burials were created along this shore for ~900 years. This period, from ~3670 to 2760 cal. BP, spans from the end of the Early Bronze Age to the advent of the EIA. The burial gap may mark disruption of local foraging populations through incursions by non-local pastoralists. Radiocarbon dates on faunal remains indicate that domestic herd animals first appear around 3275 cal. BP, just prior to the first EIA human burials. Stable carbon and nitrogen isotope analysis of human remains and zooarchaeological data indicate that domestic fauna were minor dietary components for EIA people. Like preceding foragers, the EIA groups relied extensively on Baikal's aquatic food sources, indicating that the scale of pastoralism during this period was relatively limited.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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.

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
Published2017
Admission routes1
Has abstractyes

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