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Record W7048069876

Jos Amman von Ravensburg, Zanetto Bugatto e la parte fiamminga nella pittura lombarda di eta sforzesca.

2023· article· it· W7048069876 on OpenAlexaboutno aff

Bibliographic record

VenueFlorence Research (University of Florence) · 2023
Typearticle
Languageit
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFlemishStudioPaintingQuarter (Canadian coin)PianoExhibition
DOInot available

Abstract

fetched live from OpenAlex

Studio in parallelo di due pittori che contribuirono in maniera decisiva all'introduzione della maniera fiammingheggiante fra Liguria e Lombardia nel terzo quarto del Quattrocento, Jos Amman von Ravensburg, noto per un'Annunciazione firmata e datata 1451 in Santa Maria di Castello a Genova, di cui viene ricostruita una successiva attività milanese, e Zanetto Bugatto, pittore lombardo formatosi nella bottega di Rogier van der Weyden a Bruxelles fra 1461 e 1463, per cui viene argomentata l'identificazione col Maestro della Madonna Cagnola e rivendicato un ruolo di primo piano nella Lombardia sforzesca, a lato di Vincenzo Foppa e di Bonifacio Bembo. A parallel study of two painters who made a decisive contribution to the introduction of the Flemish manner between Liguria and Lombardy in the third quarter of the 15th century, Jos Amman von Ravensburg, known for an Annunciation signed and dated 1451 in Santa Maria di Castello in Genoa, whose later activity in Milan is reconstructed, and Zanetto Bugatto, a Lombard painter who trained in Rogier van der Weyden's workshop in Brussels between 1461 and 1463, for whom the identification with the Master of the Madonna Cagnola is argued and a leading role in Sforza Lombardy is claimed, alongside Vincenzo Foppa and Bonifacio Bembo.

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)0.000
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 score0.033
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.047
GPT teacher head0.297
Teacher spread0.250 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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