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Record W7135662804

Silesian Baroque Cut Glass in the collection of Museum of Decorative Arts in Prague

2012· dissertation· cs· W7135662804 on OpenAlexaboutno aff
Tereza Bednářová

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2012
Typedissertation
Languagecs
FieldArts and Humanities
TopicHistorical and Cultural Studies of Poland
Canadian institutionsnot available
Fundersnot available
KeywordsBaroqueDecorative artsQuarter (Canadian coin)The artsStained glassPolychrome
DOInot available

Abstract

fetched live from OpenAlex

The Diploma thesis "Silesian Baroque Cut Glass in the collection of Museum of Decorative Arts in Prague" deals with the collection of Silesian Baroque cut glass at the Museum of Decorative Arts in Prague spanning from the last quarter of the 17th century to the third quarter of the 18th century. The thesis describes the technique of glass cutting and its history. It also deals with the beginnings of glass cutting in Silesia, which are associated with the Schaffgotsch family in the Hirschberger Valley. Worth mentioning is also the renowned glass cutter Friedrich Winter, who along with his glass workshop substantially influenced Silesian glass cutting in the following decades. The thesis also mentiones other well-known glass cutters, particularly Christian Gottfried Schneider. The chapters dealing with the development of the shape and ornament of Silesian Baroque cut glass are important for correct classification by periods and localities. A catalogue of selected objects from the collection of the Museum of Decorative Arts in Prague follows the first part of the thesis which comprises the text. Most of the catalogue's objects have not been published thus far or were often never presented in detail if they appeared in literature.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.010

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 head0.014
GPT teacher head0.237
Teacher spread0.222 · 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
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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