Rembrandt as a Painter: New Technical Research. Introduction
Bibliographic record
Abstract
This article provides an introduction to this special issue of ArtMatters presenting essays developed from the online international symposium New Technical Research on Rembrandt: Paintings, Drawings, Prints organised by the Städel Museum, Frankfurt, in January 2022. Focusing on paintings, seven papers report on recent technical investigations of works by Rembrandt and his studio in the collections of the Städel Museum; the Agnes Etherington Art Centre, Queen’s University, Kingston, Canada; the Ashmolean Museum, Oxford; the Gemäldegalerie Alte Meister, Hessen Kassel Heritage; the Gemäldegalerie, Staatliche Museen zu Berlin; the Hessisches Landesmuseum, Darmstadt; the Mauritshuis, The Hague; and the National Gallery of Art, Washington, DC. Co-authored by art historians, conservators and scientists, these essays explore how current technologies and methods can shed new light on Rembrandt’s painting techniques and workshop practice while pointing the way to future research. This introduction summarises key themes and discoveries that tie these studies together.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".