The Use and Deterioration of Intumescent Fire-Retardant Paint on Louise Nevelson’s Erol Beker Chapel of the Good Shepherd
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".