The Fire at the Royal Saskatchewan Museum, Part 1: Salvage, Initial Response, and the Implications for Disaster Planning
Bibliographic record
Abstract
A massive fire at the Royal Saskatchewan Museum in Regina, Canada, in 1990 demonstrated that the initial response phase following a fire is a crucial period when priorities are established and decisions are made that will determine the success of collection recovery. A chronology of the fire response shows the speed at which decisions are made within the chaotic aftermath of a disaster. At the Royal Saskatchewan Museum, several factors contributed to an environment that slowed the initial response to soot damage and hampered collection recovery. These factors are analyzed with respect to improving the position of collections recovery within the larger context of the recovery of the entire museum, its programs, and its public image. The museum conservators learned the importance of an overall view in disaster planning; in particular, they found that special attention must be paid to the balance of power during a disaster response because it will determine whether plans for collection recovery can be acted upon in a real disaster situation. During the fire response and recovery, museum conservators also learned about the unique characteristics of soot and developed effective procedures for the initial response to soot and the salvage of soot-covered objects. Details of soot removal and cleaning during the recovery phase are discussed in another article in this issue.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".