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Record W3008851952 · doi:10.37445/adiu.2018.01.04

ARCHAEOLOGICAL MAPS OF THE SOUTH UKRAINE

2018· article· en· W3008851952 on OpenAlexaboutno aff
A. O. Korvin-Piotrovskyi

Bibliographic record

VenueArchaeology and Early History of Ukraine · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsArchaeologyQuarter (Canadian coin)Period (music)DozenHistoryHistorical archaeologyPrehistoric archaeologyPost-medieval archaeologyArchaeological evidenceGeographyPrehistoryArt

Abstract

fetched live from OpenAlex

Archaeological investigations of southern regions of our country have a long story. By the efforts of amateurs and connoisseurs of antiquities, and as time academics were discovered hundreds of the new sites that got its place on archaeological maps. The history of archaeology operates by dozen archaeological maps created since 2nd quarter of 19th century to the present day. They were good spotlighted of territories exploration degree at certain stages of scientific development, illustrated priority subject matters for researchers, and the level of demand of special knowledge and instruments required for creating a qualitative cartography product. A significant role in the emergence of archaeological maps of the region played by Odesa Society of History and Antiquities and Kherson museum, Archaeological Congresses, large-scale archaeology investigations of 1960s—80s. Archaeological cartography was born within science since 19th century and on the crossroads of centuries is make a claim for being separate science line. But in Soviet Period it was relegated to almost illustrative only. And even still it had not become a powerful tool as in archaeology, and more, in the field of protection of archaeological sites.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.997

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 studies0.0000.025
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.218
Teacher spread0.197 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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