Mineralogical and art-historic traits in investigation of 'anonymous' paintings
Bibliographic record
Abstract
Laboratory X-ray micro-diffraction (μXRD) is becoming more widely applied to inorganic phase analysis of artworks samples and there is a growing demand to give relevant answers to questions about the detection limits or whether the quantitative phase analysis (QPA) can also be performed using the micro-diffraction setup.To be able to give such answers a comparison of the micro-diffraction setup with the most commonly set-ups (reflection and transmission) in the quantitative phase analysis, binary and ternary mixtures of azurite, malachite, calcite and quartz in known ratios were prepared and analyzed.Quantitative phase analysis of all experimental data was done using Rietveld method.It was found that the accuracy of results of quantitative analysis highly depends on the sample preparation.The most accurate results were obtained in transmission geometry.Model paint layers that simulate typical paint layers of artworks were also prepared.Fragments of these model layers were analyzed by µXRD.The success of microdiffraction analysis depends markedly on the homogeneity of analyzed layers because of smaller irradiated area.The accuracy of X-ray micro-diffraction on fragments falls in between that of the other two methods performed on powder mixtures.Detection limits of micro-diffraction were studied and it was revealed that approximately 5 weight percent of phase in a mixture is needed both for reliable qualitative and quantitative analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".