Regional Accessibility of Post-Earthquake Emergency Healthcare in Metro Vancouver, BC
Bibliographic record
Abstract
The system under investigation is the regional network of emergency healthcare facilities and transportation infrastructure in Metro Vancouver, British Columbia. This dataset was developed to support a regional assessment of healthcare accessibility following a major earthquake hazard, specifically under a suite of physics-based Cascadia Subduction Zone (CSZ) M9 earthquake scenarios. The dataset integrates spatial information on hospitals with emergency departments, bridges, and the regional road network, along with estimated seismic damage states for healthcare and bridge assets based on seismic fragility analysis. The data address the research problem of quantifying how earthquake-induced physical damage affects access to critical medical services. Hospital functionality and bridge performance are combined with accessibility metrics based on the Enhanced Two-Step Floating Catchment Area (E2SFCA) method to evaluate system-level interdependencies and spatial variations in emergency healthcare availability after an earthquake. The dataset can be reused to reproduce or validate regional accessibility assessments or incorporate information for multi-hazard assessments of regional infrastructure. The data are valuable for researchers, infrastructure planners, and emergency management professionals seeking to improve the resilience of interconnected health and transportation systems. All documentation required for use of the data set is included in the Data Report.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".