Bibliographic record
Abstract
Many infrastructure networks rely on each other to deliver utilities and services to the community. In the event of a disaster, these networks can sustain significant damage. It is therefore important to identify interdependencies among networks to mitigate the disaster consequences. In 2003, Public Safety Canada (PSC) and NSERC initiated the Joint Infrastructure Interdependencies Research Program (JIIRP) for this purpose. The research was carried out at six Universities across Canada including the University of British Columbia (UBC). The aim of JIIRP at UBC was to study infrastructure interdependencies during disasters in order to aid in decision making. This involved the development disaster simulation methodology and tool, and the implementation of a case study. UBC's Point Grey campus was used as case study. The campus is located in southwestern British Columbia, a known seismic zone, therefore earthquake disaster scenario was chosen. Reasonable estimations of the expected seismic damage and losses are required in order to simulate a realistic disaster scenario. For this reason, in this thesis, seismic risk assessment was carried out for the buildings at UBC. This involved the development of a building database, the assessment of the expected level of damage to the structural and nonstructural building components, and the estimation of monetary, human and functionality losses. Buildings in the database were classified into prototypes and the damage was estimated for several levels ofintensity using damage probability matrices. As expected, the most vulnerable buildings on campus were those containing unreinforced masonry. These buildings make up 7% of the buildings on campus. The least vulnerable buildings were multi-family residential woodbuildings which account for 27% of the buildings on campus. Losses were estimated following the damage assessments. Casualties were estimated for three times of day. 2PM was determined to be the critical time of day as the campus population is the greatest at this time. Monetary loss and functionality trends were examined with respect to earthquake intensity and it was shown that for moderate intensity earthquakes, the losses depend primarily on nonstructural damage, while structural damage plays the most important role for higher intensities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".