A Quantitative Approach to Assess Seismic Vulnerability of Touristic Accommodations: Case Study in Montreal, Canada
Bibliographic record
Abstract
In many places of the world, the interruption of touristic activities in the aftermath of a catastrophic earthquake is often neglected in the evaluation of seismic risks; however, these activities can account for a significant proportion of short-term and long-term economic impacts for these regions. In the last decade, several rapid visual screening techniques have been developed to define the typology of buildings and to estimate their seismic vulnerability and potential for damage. We adapted the existing screening procedures that have been developed for generic buildings to specific circumstances that are most common for tourist accommodations. The proposed approach considered six criteria related to structural and nonstructural elements of buildings, as well as local soil conditions. A score was assigned to each criterion as a function of the capacity of the elements to resist ground shaking. A vulnerability index in four levels of building vulnerability was developed combining the scores of the six criteria. The approach was tested in a pilot area of Montreal to a set of 70 typical buildings grouped in four categories based on their accommodation capacity. In Montreal, tourism is an important source of income for the city where 351,000 room-nights were booked with total stay expenditures of CAD 4.9 billion in 2019. The results indicated potential significant disruptions in activities related to tourism; 46% of the buildings investigated have a high to very high vulnerability index. Among them, 4/5 are located in the old city and 1/5 in the downtown area of the pilot zone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".