Development and application of a computer simulation framework for assessing disaster recovery in urban communities
Bibliographic record
Abstract
In this dissertation an object-oriented framework of models is developed and applied to study disaster recovery in communities in British Columbia, Canada. The impact of earthquakes on communities is quantified over months and years, and the focus is on identifying the factors that affect the recovery. Contrasting with the practice of investigating disaster impacts to infrastructure or societal systems in isolation, an integrated approach is used in this dissertation. Lifelines, buildings, and persons are modelled in the same computational environment. One contribution of this dissertation is the development of models for infrastructure and social systems of a community. Another contribution is the development of a new approach to simulate the transportation of goods through a network of models. This new approach allows great flexibility in the composition of the transported goods and facilitates the modelling of the competition for resources. Another innovation is the individual modelling of buildings and dwellings, in this work referred to as dwellings, in the community. The socioeconomic demographics of the dwellings determine their capacity to compete for limited resources, which affect their recovery capacity. The integration of socioeconomic demographics, infrastructure, and buildings in the same computational environment allows for a broad range of disaster mitigation actions to be compared. This dissertation assesses the benefits of improving resource management, retrofitting physically vulnerable infrastructure, improving access to funds for recovery, among other actions. The findings in this dissertation can inform pre-disaster plans and help identifying mitigation strategies that improve disaster recovery in communities in British Columbia.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".