How do we assess mould levels? Testing the parameters of rapid adenosine bioluminescent swabs in conservation
Bibliographic record
Abstract
Successfully detecting and treating mould on cultural heritage to a ‘safe’ level is an important concern due to the variety of remediation approaches, substrates and health hazards posed by such fungi. In conservation, rapid adenosine bioluminescent swab testing has been used in two main applications: to identify if fungi are present, and to attempt to quantify if a remediated object is ‘clean enough’ for regular use or storage. A literature review across the food hygiene, healthcare and conservation sectors was combined with a simple lab experiment comparing RLU (Relative Light Unit) values obtained using a Kikkoman PD-30 lumitester with LuciPac Pen swabs on various surfaces against low magnification, high magnification and SEM imaging of the same samples. Results show it is impossible to establish numerical benchmarks for ‘clean enough’ and furthermore confirm that the devices are not ideal for diagnosing what is and is not mould. However, tracking the efficacy of remediation processes with the devices was successful when three sample areas were compared: a visually clean area before treatment, a visually mould damaged/dirty area before treatment, and the latter after a mould remediation treatment. This suggests that rapid adenosine bioluminescent swab testing can provide supporting evidence to conservators to make more informed decisions about how effective cleaning processes are for a particular artefact substrate.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".