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Record W3038017480 · doi:10.37558/gec.v17i1.759

Mapeo de fluorescencia inducida por láser de pigmentos en murales secco pintados

2020· article· es· W3038017480 on OpenAlexfundno aff
María Auxiliadora Gómez‐Morón, Rocío Calderón, Franceso Colao, R. Fantoni, Javier Becerra Luna, Pilar Ortiz

Bibliographic record

VenueGe-conservacion · 2020
Typearticle
Languagees
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
FundersInstitute of Aboriginal Peoples Health
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

La fluorescencia inducida por láser es una técnica de análisis a distancia, aplicada con éxito en tiempo real para el diagnóstico de obras de arte, permitiendo la observación de características invisibles al ojo humano, como rastros de retoques o la presencia de consolidantes modernos.
 El objetivo de este artículo es generar una base de datos de pigmentos históricos con sus respectivos aglutinantes y consolidantes, realizada para respaldar la identificación remota y el mapeo de estos materiales en un mural de la forma menos invasiva posible. Para este objetivo, se ha utilizado una fuente láser monocromática ultravioleta que emite a 266nm con escaneado remoto en combinación con reflectancia. Se realizaron modelos de pintura mural en técnica a secco de acuerdo con las recetas tradicionales del siglo XVII.
 Análisis digital de imagen, análisis de componentes principales y mapeado de ángulo espectral ha sido llevado a cabo para obtener los datos de mapeado de dos pigmentos seleccionados, azul esmalte y rojo carmín en una pintura mural real (siglo XVII). Esta técnica no invasiva nos permitió trabajar de manera remota, a una distancia de 11 m de la obra de arte. Los resultados son consecuentes con los microanálisis tradicionales llevados a cabo para identificar pigmentos mayoritarios.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.032
GPT teacher head0.236
Teacher spread0.205 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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