Monitoring and Mitigating Particulate Matter Deposition on Decorative Surfaces: Current and Future Approaches in the Palace of Westminster
Bibliographic record
Abstract
Research into dust deposition rates on the wall paintings in the State Apartments at the Palace of Westminster, London, UK, began during the restoration of the encaustic floor tiles. The study has broadened to inform day-to-day preventive care for the extensive fine art collections on display and the intricately decorated Gothic interiors, providing a powerful tool for the forthcoming restoration and renewal of the Palace. Different monitoring methods, using optical microscopy, macro-photography and software-based image analysis, were investigated. Qualitative analysis with SEM-EDX and optical microscopy allowed the identification of a number of anthropogenic, geogenic and biological sources of particulate matter, while quantitative results elucidated deposition trends, highlighting both seasonal and works-related impacts. Results indicated that mitigation measures taken to protect works of art and limit the diffusion and deposition of particulate matter on surrounding surfaces were successful. A new dust monitoring method, based on imaging of vertical surfaces and on a recently developed image analysis workflow (CHIJ) operated in open-source software (ImageJ) was trialled alongside more traditional methods for measuring dust deposition through collection of particulate matter on proxies. Results showed significant discrepancies between data acquired directly on wall painting surfaces as compared to horizontal glass slides. The advantages, limitations and complementarity of both monitoring methods were identified, and their potential contributions to the development of data-driven conservation approaches for heritage sites were assessed. The relatively low-tech methods and equipment used present useful and adapted tools for collection managers and conservators to inform their decision-making processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".