Assessing the impact of tropical cyclones on economic sectors in Costa Rica, Central America
Bibliographic record
Abstract
Tropical cyclones (TC) pose a persistent natural hazard to Costa Rica. Exposure to natural hazards, such as mass movements and floods, is compounded by a growing urban population and inadequate land use planning. This study conducted a comprehensive analysis of the economic impacts of TC of Costa Rica from Hurricane Joan in 1988 to Hurricane Eta in 2020, assessing the impact by municipality and economic sector using baseline information of the Ministry of National Planning and Economic Policy. According to the study, road infrastructure (933.8 US million), agriculture (280.5 US million), river rehabilitation (153.96 US million), housing 98.26 (US million), and health (81.74 US million) were among the sectors most severely affected by TC over the past 30 years. The Pacific basin municipalities in Costa Rica were found to be the most vulnerable, primarily due to the indirect impacts of TC. The study's results offer useful information on the economic sectors and municipalities that are most exposed from TC in Costa Rica and provide a replicable methodology for other regions and countries facing similar tropical phenomena.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".