Occurrence of <i>Aspergillus niger</i> strains on a polychrome cotton painting and their elimination by anoxic treatment
Bibliographic record
Abstract
This study aimed to isolate and identify the population of filamentous fungi colonizing a cotton painting, whose conservation status was compromised and showed signs of biodeterioration due to dirt accumulation and microbial metabolism. In addition, microbiological techniques such as cultivation-dependent approach and molecular biology were used to identify microbial populations and to eliminate their metabolic action. For this, the nondestructive anoxic atmosphere technique was used, in which the microbial metabolism was affected by the absence of oxygen. Prior to exposure to an anoxic atmosphere, only one fungal species, Aspergillus niger, was identified at 12 points sampled in the obverse and reverse of the artwork; no fungal species persisted as a result of anoxic treatment. These results showed that exposure to anoxic conditions was effective for the total elimination of isolated fungal strains as well as their spores. In conclusion, this study proved the unprecedented effectiveness of a nondestructive technique for artwork on textile colonized by black fungi species. Thus, this interdisciplinary work involving conservation, microbiology, and chemistry presents a tool to eliminate microorganisms, while maintaining the integrity of artwork and safety of the restorer, that can be applied prior to artwork restoration.
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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".