Urban disaster management: a case study of earthquake risk assessment in Cartago, Costa Rica
Bibliographic record
Abstract
Natural hazards pose a threat to population, its goods and the environment. Urban areas are particularly vulnerable not only because of the concentration of population but due to the interplay that exists between people, buildings, and technological systems. Disasters have the potential to destroy decades of investment and effort, and cause the deviation of resources intended for primary tasks such as education, health and infrastructure. Disaster management is therefore an important component of urban planning and management as disasters pose a serious threat to sustainable development.There are basically three very important weaknesses in the way disaster management is currently being carried out. The first relates to the reliance upon hazard zonations alone rather than using risk as input for the selection and prioritisation of mitigation strategies. This is unfortunately in part due to the lack of empirical-historical data on damage and due to the high costs of generating and updating building inventories. The second relates to the reliance upon response rather than a concerted effort in both the pre-disaster and the postdisaster phases. The last relates to the lack of disaster information networks which coordinate efforts amongst the many institutions involved.The case of the Costa Rican city of Cartago was chosen as an example of the challenges that lie ahead in terms of geo-information for urban disaster management. The city provides an interesting case study; it represents a typical example of a medium-sized Costa Rican city that is located in a highly hazard-prone area. Cartago is also representative of a financially constrained local government authority with very basic baseline information where plans are elaborated without proper disaster-related information inputs.The research addresses building and population risk by integrating a hazard intensity map, damage curves derived from historical damage records and a building inventory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".