Illuminating intuition with evidence: assessing collection risks within Museums Victoria's exhibitions
Bibliographic record
Abstract
Collection risk assessments were conducted for each of Museum Victoria’s three exhibition venues, using a method based on the Cultural Property Risk Analysis Model (CPRAM) to identify, characterise, and quantify risks. The results of the assessments showed that cumulative light exposure was, by far, the highest risk to collections on display. However, other results were less intuitive. Water leaks and pest infestations made up a large percentage of recorded incident reports but ranked very low in terms of overall expected loss to the collection. Loss due to seismic activity ranked higher than expected, given the popular perception that seismic activity need not concern those living in the Melbourne region. The assessments also highlighted which object populations are more likely to suffer damage. Plastic materials, fluid-preserved specimens, objects on open display, and objects on very long-term display were found to be most at risk. The results of the assessments were illuminating and will inform future exhibition design and maintenance programmes. Additionally, the results identified existing data gaps and thus also identified areas of research that will benefit collection care. Such research—for example, microfading tests—will enable future risk estimates to become increasingly realistic and validated.
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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 | 0.072 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".