Bibliographic record
Abstract
This research began with the Joint Infrastructure Interdependencies Research Program (JIIRP). JIIRP was part of an effort by the Government of Canada to fund research to develop innovative ways to mitigate large disaster situations. An interdependency simulator (I2Sim) was developed in the University of British Columbia through this project. This tool was developed to take into account the dynamic changes of the functional conditions of any given system. This thesis makes two major contributions to the capability of the simulator’s methodology, to handle seismic events and events that affect dense concentrations of people. The distinguishing characteristic of an earthquake event can affect the city and all the surrounding regions, causing damage to all lifeline systems. In its original form, I2Sim could model the damage and impact of each system on its own, but was unable to account for the effects of all other systems. The interdependency between systems is a crucial element for determining the impact of an earthquake and the time for recovery. The methodology proposed here can be used to measure Interdependencies and Resiliency in a region. Two cases were studied and implemented to test the methodology and the simulator. The first one was an earthquake hazard in a relatively small region (UBC Campus) in which the interdependencies and resiliency would be revealed to the emergency managers of UBC Campus; the second one, was a localized event in a massive sporting event (Winter Olympics in Vancouver), a black out in a Football Stadium that caused an uncontrolled egress, and related casualties due to a collapsing stage and the evacuation process were modelled. With the methodology and the simulator (I2Sim) it is possible to build up Region models, Disaster Scenarios, Objective Functions and Emergency Planning; and these, along with Interdependency and Resiliency calculations, will help in the preparedness, planning, response and recovery phases of any disaster.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".