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Record W4409140785 · doi:10.3390/heritage8040128

The Use and Deterioration of Intumescent Fire-Retardant Paint on Louise Nevelson’s Erol Beker Chapel of the Good Shepherd

2025· article· en· W4409140785 on OpenAlexaff
Lora V. Angelova, Aaron Shugar, Christopher A. Maines, Satoko Tanimoto, Jean Dommermuth, Hannah Duggan, Teresa T. Duncan, Adam C. Finnefrock, Jennifer L. Mass

Bibliographic record

VenueHeritage · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsQueen's University
FundersU.S. General Services Administration
KeywordsIntumescentChapelFire retardantArtArt historyComposite materialMaterials science

Abstract

fetched live from OpenAlex

Louise Nevelson’s Erol Beker Chapel of the Good Shepherd (1977) is a sculptural environment consisting of wooden sculptures painted a monochromatic white color. The paints show signs of degradation including cracking, chipping, peeling, and the formation of blisters and powdery efflorescence. A significant amount of pentaerythritol (PER) detected during a former analysis was concluded to originate from an alkyd paint. We show that the PER originates from the PVAc paint on the sculptures, which we have determined to be an intumescent, fire-retardant (IFR) coating. IFR paints and coatings are functional materials designed specifically to delay the combustion of their substrate. At least one other sculpture by Louise Nevelson is known to have been painted with an IFR coating. Our analyses by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), pyrolysis–gas chromatography/mass spectrometry (Py-GCMS), and cross-section microscopy show the presence and distribution of common IFR additives including PER, dicyandiamide, melamine, inositol, ethylenediamine, and phosphates. These are present throughout the PVAc paint and are enriched in the powdery efflorescence. In addition, the degradation behavior of the paint is typical for IFR coating systems that have been exposed to uncontrolled environmental conditions and especially high humidity events.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.277
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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