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Record W3188049637 · doi:10.1515/opar-2020-0169

Ceramic Traditions in the Forest-Steppe Zone of Eastern Europe

2021· article· en· W3188049637 on OpenAlexaboutno aff
Константин Михайлович Андреев, Alexander Alekseevich Vybornov

Bibliographic record

VenueOpen Archaeology · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicAncient and Medieval Archaeology Studies
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsPotterySteppeArchaeologyGeographyQuarter (Canadian coin)Period (music)Forest steppeAncient historyHistoryArtForestry

Abstract

fetched live from OpenAlex

Abstract Early pottery on the territory from the Eastern Caspian Sea and Aral Sea to Denmark reveals a certain typological similarity. It is represented by egg-shaped vessels with an S-shaped profile of the upper part and a pointed bottom. The vessels are not ornamented or decorated with incised lines, organized often in a net. This type of pottery was spread within hunter-gatherer ancient groups. The forest-steppe Volga region is one of the earliest centers of pottery production in Eastern Europe. The first pottery is recorded here in the last quarter of the seventh millennium BC. Its appearance is associated with the bearers of the Elshanskaya cultural tradition. The most likely source of its formation is the territory of Central Asia. Later, due to aridization, these ceramic traditions distributed further westward to the forest-steppe Don region. During the first half of the sixth millennium BC, groups associated with the bearers of the Elshanskaya cultural tradition moved westward. Significant similarities with the ceramic complexes of the Elshanskaya culture are found in materials from a number of early pottery cultures of Central Europe and the Baltic (Narva, Neman, and Ertebølle).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.279
Teacher spread0.209 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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