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THE CASE OF CAPOGROSSI IN ROME: COLLECTING DATA WITH DIFFERENTTECHNOLOGIES ON A CONTEMPORARY MURAL PAINTING

2017· article· en· W2615944559 on OpenAlexfundno aff
Paola Mezzadri, Jillian Russo

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory Health
KeywordsMuralDocumentationPaintingPresentation (obstetrics)Visual artsArtThematic mapComputer scienceArt historyCartographyGeography

Abstract

fetched live from OpenAlex

Abstract. This paper focuses on the presentation of a part of the main thematic data documenting the pathologies and the degradation problems of a contemporary mural painting, which was designed and carried out by the italian artist Giuseppe Capogrossi in 1954. This forgotten masterpiece is developed on the ceilings of the main double stairscase at the entrance of the Airone, an ex-cinema-theatre in Rome (Italy). In time, the original project was completely damaged and now the Airone cinema is abandoned since 1999; the decoration, strictly connected to the function of the original project, has been completely covered by synthetic coatings. The documentation of the observed pathologies and the original materials of the lower ceiling takes place during a restoration project in 2015–2016 and was accomplished by utilizing different technologies in order to facilitate the collecting of the main data within several graphic thematic tables. The challenge of this documentation was to create a contact point, and perhaps also a contamination, between the practices of CAD graphic documentation, restoration and GIS technology.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.044
GPT teacher head0.274
Teacher spread0.229 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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