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ANIMAL SCULPTURE IN RUSSIAN ACADEMIC ART OF THE XIX — FIRST QUARTER OF THE XXI CENTURIES

2024· article· ru· W4396613228 on OpenAlexaboutno aff
Ш. Ни, Marina Sorokina

Bibliographic record

Venuenot available
Typearticle
Languageru
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsSculptureModernism (music)Quarter (Canadian coin)ArtArt historyNatural (archaeology)RealismSt petersburgVisual artsHistoryArchaeology

Abstract

fetched live from OpenAlex

Анималистическая скульптура в русском искусстве отражает изменения в обществе, сочетает реализм с модернизмом. Мастера XIX в. создавали реалистичные образы животных, показывая их эмоциональную выразительность. В XX в. советские художники продолжили традицию анималистической скульптуры, применяя новаторские методы и материалы. Современные скульпторы продолжают развивать этот жанр, выражая уникальный взгляд на мир природы. В статье рассмотрены работы авторов, связанных с петербургской школой. The animalistic sculpture of Russian art reflects changes in society, combines realism with modernism. The masters of the XX century created realistic images of animals, showing their emotional expressiveness. In the XX century, Soviet artists continued the tradition of animalistic sculpture, using innovative methods and materials. Modern sculptors continue to develop this genre, expressing a unique view of the natural world. The article examines the artists of the St. Petersburg school.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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