Mapeo de fluorescencia inducida por láser de pigmentos en murales secco pintados
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".