Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Caribbean region is highly vulnerable to multiple hazards. Resultant impacts may be derived from single or multiple cascading risks caused by hydrological-meteorological, seismic, geologic, or anthropological triggers, disturbances, or events. Studies suggest that event records and data related to hazards, risk, damage, and loss are limited in this region. National Disaster Risk Reduction (DRR) planning and response require data of sufficient quantity and quality to generate actionable information, statistical inferences, and insights to guide continual policy improvements for effective DRR, national preparedness, and response in both time and space. To address this knowledge gap, we review the current state of knowledge, data, models, and tools, identifying potential opportunities, capacity needs, and long-term benefits for integrating Earth Observation (EO) understanding, data, models, and tools to further enhance and strengthen the national DRR framework using two common disasters in Jamaica: floods and landslides. This review serves as an analysis of the current state of DRR management and assess future opportunities. Equally, to illustrate and guide other United Nations Disaster Risk Reduction (UNDRR) priority countries in the Pacific region, known as Small Island Developing States (SIDS), to grapple with threats of multiple and compounding hazards in the face of increasing frequency, intensity, and duration of extreme weather events, and climate change impact.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it