Climate change and flood susceptibility in Bocas del Toro, Panama: A multi-criteria spatial analysis approach
Bibliographic record
Abstract
Though Bocas del Toro province lies in the Panamanian Caribbean region regarded as "ground zero" in the global climate emergency, responses to frequent inland flooding remain reactive due to the lack of a comprehensive flood hazard map. This study integrated publicly available spatial data with elicitation from regionally-specific subject matter experts to map present and future flood prone zones in the province using the Analytical Hierarchy Process (AHP) and Weighted Sum tool, based on the shared socioeconomic pathways (SSP). Rainfall and temperature data were analyzed to predict future flood related extreme events. Eight flood-conditioning factors (elevation, slope, topographic wetness index, drainage density, distance from rivers, flow accumulation, Normalize Different Vegetation Index, and land cover) were integrated with extreme historic and projected rainfall using AHP-derived weights to derive the maps. To assess flood-zone sensitivity, rainfall weight was reduced by 5 % and sequentially reallocated to the other factors. Meteorological data collected locally showed no significant temporal trend in extreme rainfall and heat as projected. Flood susceptibility maps, validated with an AUC of 0.98, revealed that Changuinola has the highest proportion of current (30.38 %) and projected flood-prone areas under SSP2-4.5 (35.06 %) and SSP5-8.5 (30.76 %), while Almirante was projected to experience the greatest spatial expansion under both scenarios. While flood-prone zones in Almirante and Changuinola were most sensitive to distance from rivers and flow accumulation respectively, those of Bocas del Toro and Chiriquí Grande were most sensitive to elevation. The study recommends proactive mitigation through controlled development near waterways, elevation-informed land use planning, and preservation of natural vegetation. Future research should assess the impact of land use change on predicted flood zones or map areas susceptible to marine-driven flooding not covered in this study.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".