Cultural Heritage Environments: Monitoring Strategy for Preventive Conservation of Cultural Assets and Human Health Protection
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.
Bibliographic record
Abstract
Objects of historic artistic value, conserved in indoor deposits or exhibited inside museum halls, are strongly influenced by the environmental parameters, as temperature, relative humidity and light quality. Environmental parameters directly impact the structural integrity of constitutive materials and promote microbial colonization on artwork surfaces, leading to biodeterioration. In cultural heritage dedicated environments (CHE), the microbial load may exist both on art works surface and in the environmental aerosol (bioaerosol), maintaining a unique balance. In this study, through a multi-phasic approach the presence of bacteria and fungal colonies in the aerosol and artifacts surface, of an exposure hall, have been investigated. This study defined specific, non-invasive procedures to sample microbial colonies, spread both on artworks surface and in the aerosol of dedicated indoor environments. Results from morphological analysis (microscopy, in vitro culture) and molecular investigation (microbial genomic DNA), provided useful information on the composition of the microbial consortia, allowing a complete understanding. Microorganisms, in addition to inducing artifacts biodeterioration are able to produce and release, in the aerosol (bioaerosol) of surrounding environment, biological particles and molecules (spores, cellular debris, toxins and allergens), potentially dangerous for the health of operators and visitors. The complete understanding of the consortia is peculiar to counteract the microbial colonization, also performing green strategies.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it