Juan Fernández el Labrador, Miguel de Pret y la «construcción» de la naturaleza muerta
Bibliographic record
Abstract
Entre marzo y junio de 2013 el Museo del Prado albergó una exposición monográ¿ ca dedicada a uno de los bodegonistas españoles más enigmáticos del siglo XVII, Juan Fernández, llamado el Labrador. Con motivo de la muestra se procedió al estudio técnico de todas las obras expuestas, aprovechando la circunstancia de haber reunido un importante número de obras de diferentes procedencias. El presente artículo recoge los resultados obtenidos y las conclusiones a las que se ha llegado tras la investigación. Con ello, se abre una nueva vía para el debate del género del bodegón en el siglo XVII y se per¿ la la ¿ gura de dos artistas de enorme interés en este contexto: Juan Fernandez el Labrador y el prácticamente desconocido Miguel de Pret.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".