Risk-based Decision-making Informed by Analysis of an Early Nineteenth-century Manuscript Containing Smalt
Bibliographic record
Abstract
This paper reviews a collaborative examination and analysis of an early nineteenth-century music manuscript at the Canadian Conservation Institute. Collaboration between conservation scientists and conservators helped process scientific information, hazards, and treatment decisions to complete the arc of planning, treatment execution, and future care recommendations for the client. An unexpected result during the initial analysis of the pigments flagged the presence of arsenic on the painted and unpainted areas of the textblock. Initial hypotheses were that it could have derived from an application of pesticide on the binding or that it was part of the papermaking process. To characterize the nature of the arsenic more fully and to attempt to understand the level of risk during handling, further analysis was carried out. Through the analysis, it was determined that the source of arsenic in the manuscript is smalt – a blue glassy colourant, added to the paper during manufacture to make it appear whiter; there was no indication of an arsenical pesticide found. This case study provoked interesting discussions regarding the contextualizing of risk and analysis results when working with an unexpected finding of potential hazards, both during the execution of a conservation treatment, and in recommending care during handling and storage.
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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 | 0.047 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".