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Record W2276071247 · doi:10.1255/jnirs.1198

Identification of Historic Artists' Pigments Using Spectral Reflectance and X-Ray Diffraction Properties I. Iron Oxide and Oxy-Hydroxide-Rich Pigments

2016· article· en· W2276071247 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Near Infrared Spectroscopy · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of CanadaBrown UniversityNational Aeronautics and Space Administration
KeywordsPigmentMineralIron oxideMaterials scienceHydroxideMineralogyChemistryAnalytical Chemistry (journal)Inorganic chemistryMetallurgyEnvironmental chemistry

Abstract

fetched live from OpenAlex

The chemical and mineralogical analyses of art have important cultural, scholarly and historic applications. Investigations can be performed to learn about the history of a particular object, or to investigate techniques for conservation and restoration purposes. In this study, a suite of historic artists' pigments, synthetic samples and end members were analysed to detect specific characteristics that can be applied to their identification and differentiation from other pigments. We combined and compared reflectance spectroscopy (RS; 350–2500 nm) with X-ray diffractometry (XRD) for this purpose. We focused on pigments rich in iron oxides and oxy-hydroxides, specifically ochres, siennas, umbers and “red oxides”. It was found that these two techniques are often complementary, and have different strengths and weaknesses. XRD was found to be able to detect a wider range of accessory minerals than RS, and its strength lies in discrimination on the basis of mineral structure. It is less sensitive than RS for detection of poorly crystalline/amorphous phases and fine-grained components. RS is very sensitive to detection and discrimination of different Fe-oxy-hydroxides in the wavelength region below ∼1200 nm. At longer wavelengths (>1200 nm), reflectance spectra can detect the presence of accessory minerals that possess strong absorption features. The analytical strength of RS lies in discrimination on the basis of composition, which is usually linked to specific crystallographic structures. RS and XRD data acquired for powdered mineral pigments can be successfully integrated for the identification of sub-groups within the iron oxy-hydroxide-rich group of pigments, but require further investigation for individual sample discrimination. RS and XRD are also able to verify, disprove or refine identification of the phase(s) that make up these pigments. A major practical advantage of RS over XRD is that RS is a non-destructive, non-contact technique, whereas XRD normally requires removal of a sample from a cultural artefact. One of the main findings is that the reflectance spectra of the pigments present in the mixtures retained their diagnostic absorption features even when mixed with linseed oil. Our results show that RS can be used to discriminate various Fe oxy-hydroxide-rich pigments as well as confirm the organic nature of binders; this has important implications for restoration and conservation of cultural artefacts.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.249
Teacher spread0.220 · 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