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Record W3195131080

ИСТОРИЧЕСКАЯ ТОПОНИМИЯ КОРЕЙСКИХ ПОСЕЛЕНИЙ НА РОССИЙСКОМ ДАЛЬНЕМ ВОСТОКЕ (ВТОРАЯ ПОЛОВИНА XIX - НАЧАЛО XX В.)

2004· article· ru· W3195131080 on OpenAlexaboutno aff
Джарылгасинова Роза Шотаевна

Bibliographic record

VenueЭтнографическое обозрение · 2004
Typearticle
Languageru
FieldSocial Sciences
TopicNight-time city culture
Canadian institutionsnot available
Fundersnot available
KeywordsMemoirQuarter (Canadian coin)HistoryPeriod (music)Human settlementRussian historyState (computer science)Ancient historyGeographyArt historyArtArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The author analyzes the origins of names of Korean settlements in the Russian Far East and their transformations during the period from 1860s through the early 20 th century. In the study, the author draws on a wide array of sources, including the collections of the Russian State Historical Archive of the Far East, personal archives of N.V. Kiuner, maps and notes of Russian explorers and travelers, and memoirs of people who lived in the region in the first quarter of the 20 th century.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.016
GPT teacher head0.290
Teacher spread0.274 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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