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Record W4383957680 · doi:10.55086/sp233165177

Late Scythians’ Houses in North-West Crimea in the Second Half of 1st Century BC — First Quarter of 1st Century AD

2023· article· en· W4383957680 on OpenAlexaboutno aff
Egor Antonov

Bibliographic record

VenueStratum plus Archaeology and Cultural Anthropology · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicAncient and Medieval Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBarbarianQuarter (Canadian coin)Settlement (finance)Human settlementAncient historyPeriod (music)ArchaeologyHistoryApprenticeshipSixth centuryGeographyArtComputer science

Abstract

fetched live from OpenAlex

The article summarizes the data known about home layouts in North-West Crimea during the peak of the late Scythians’ settlements. The layouts of a number of houses (Kara-Tobe, South Donuzlav settlement) are introduced for the first time. These layouts are the first to be compared with each other and with the previously published layouts. Such comparison demonstrates that the number of planning schemes in late Scythians North-West Crimea was limited, besides, they changed over the time. In that way, Г-shaped house layouts that had prevailed during the previous stage (about 110 BC — middle 1 st century BC) became obsolescent before 20 BC. Kerkinitis layout system is dated within the same years. From 1 st century BC the houses of П-shaped layout emerged and after 20 BC they became prevalent. Г-shaped and П-shaped houses meet their analogies in Kerkinitis and Kalos Limen of the Greek period, they are well represented in other regions of the ancient world as well. Kerkinitis layout system is similar to ‘equally-parallel principle of home layouts’, typical for Scythian Neapolis and Ust-Alma settlement. This gives us the opportunity to expect that some Greek specialists or their Barbarian apprentices contributed to the exploration of the North-West Crimea. They could be similar specialists and apprentices who had earlier worked in Scythian Neapolis.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.014
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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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