A Community-Based Vulnerability Assessment of Terrestrial Flooding in the South Oropouche River Basin After Tropical Storm Bret
Bibliographic record
Abstract
The year 2017 had a very active season for hurricanes and extreme weather conditions. Hurricanes Harvey, Irma, and Maria did damage to several Caribbean islands. Even Trinidad and Tobago (T&T), a country which rarely experiences extreme weather conditions, was affected by Tropical Storm Bret. Tropical Storm Bret caused flooding in T&T, especially in the low lying South Oropouche River Basin.There is a dearth of research conducted in T&T about the impact of extreme weather conditions and flooding on communities and families. Thus, this study sought to conduct a community base vulnerability assessment (CBVA) of the impact of the Tropical Storm Bret induced flooding upon the residents of the South Oropouche River Basin.Primary data was collected via semi-structured interviews and questionnaires to conduct the CBVA. Furthermore, this study introduced a Modal Community Based Vulnerability Assessment Index (MCBVAI) to help determine which factors the residents South Oropouche River Basin are most vulnerable to.This study found that the most vulnerable residents were vulnerable largely to their building of structures at locations unsuitable for housing. Moreover, the most vulnerable residents also built structures that were not resilient to flooding and was elevated less than 4 feet (ft) off the ground. The appropriate policy response for the Government of the Republic of Trinidad and Tobago (GORTT) would be to i) establish building codes, ii) develop a comprehensive spatial planning strategy which prohibits people from building structures in unsuitable areas, and iii) implement disaster risk reduction programmes which focus on improving pre-event disaster preparedness, improving the national and local response, and promoting educational awareness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".