Bibliographic record
Abstract
The Canadian Conservation Institute (CCI) has developed a Geographic Information System (GIS) of hazards for Canadian cultural heritage institutions. The greatly increased access to open data is changing how advisory bodies like the CCI and the public can access and share information. For the purpose of investigating how a GIS approach can assist the CCI with its mandate to improve the preservation of collections, a map layer of cultural heritage institutions across Canada has been assembled and continues to be upgraded for accuracy, inclusion and detail (Fig. 1). This was combined with a collation of hazard layers; a partial list includes: seismic risk, notably expectations of earthquake severity tied to improvements in the national building code, tsunami exposure, wildfire data, hurricane, tornado, lightning density, pest distribution, and energy use indicators such as heating degree days and climate norm data. The platform allows examination of expectations around climate change driven risks such as sea-level rise, storm-incursions, permafrost melt. The GIS approach will also allow reassessments around expected changes to flood risk maps issued by jurisdictions, as well as Statistics Canada layers on population related factors such as changes in numbers of local populations, income and demographic shifts which can be stressors or opportunities. Sources have been drawn from federal, provincial, municipal, and academic evaluations of hazards, which now are more commonly published as GIS products. Mapping Canadian heritage institution's within a GIS improves our ability to: visualise and interpret to clients the relative magnitude of their local hazards, make ties to more refined local analyses, and show adjacencies to mapped historical events. From a national perspective the GIS can generate profiles of aggregated institutional exposure to the hazards, and more readily identify sub-populations of institutions for which particular risks would rank higher or lower among their concerns. This improves CCI's preventive conservation advisory service's perspective on mappable risks for any institution we deal with as clients. Ultimately, through federal initiatives in open data, it is our intention that client groups can look at the GIS for the purpose of educating themselves on hazards they would want to prepare for.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".