Social Impacts of Lifeline Losses: Modeling Displaced Populations and Health Care Functionality
Bibliographic record
Abstract
This paper discusses new approaches for modeling the social impacts of lifeline losses in disasters. It focuses on two types of impacts: displaced persons (and associated demand for public shelter), and reduction in functionality of health care facilities such as hospitals. The models are applied to Los Angeles. The shelter model simulates households' decision-making and considers socio-economic and locational factors in addition to housing damage and lifeline loss. It performs well in simulating the Northridge earthquake. In a M6.8 Verdugo Fault scenario, with much higher building damage and lifeline outage durations, as many as 212,000 households are estimated to seek public shelter. Accounting for lifelines substantially raises estimates compared to considering building damage alone. The health care model draws on empirical data to model the operational performance of a hospital's interacting systems (structural, nonstructural, lifeline, and personnel) in an earthquake. Results for Verdugo indicate that nearly half of L.A. county hospitals have at least a 50% chance of experiencing significant loss of functionality. The contribution of regional lifeline disruption to this loss is fairly small.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".