Structural characterisation of Eastern Canada’s old industrial masonry buildings via typological analysis: part II – building database analysis
Bibliographic record
Abstract
The typological analysis conducted in the companion paper is further expanded within this work through the critical analysis of a new extensive building database specifically conceived for old industrial masonry buildings in Eastern Canada. The identification of recurrent unreinforced masonry (URM) building archetypes in this low-to-moderate seismic region is essential to decrease uncertainties currently prevalent in the analysis of local old URM structures, often targeted for adaptive reuse. The database assembled herein comprises various heritage designated buildings across Eastern Canada and was compiled harmonising existing repositories at the federal, provincial and municipal level with an unprecedented focus on structural features. The characteristics of these resources are quantitatively analysed and applied to a case study of the city of Montréal, one of the most important industrial centres in nineteenth and twentieth century Canada. Outcomes from this study will guide practitioners and researchers involved in the structural and seismic assessment and retrofit of old industrial URM constructions, enable more and less invasive rational retrofit designs and inform the new Existing Structures provisions to be included in the 2030 National Building Code of Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".