Nature-based solutions for coastal protection in the southern Caribbean
Bibliographic record
Abstract
ABSTRACT The coastal shores of Trinidad and Tobago are at high risk (69.5 and 42.7%, respectively) of inundation from storm surges, sea level rise, and coastal erosion. The impacts of these coastal processes are predicted to worsen with climate change. Nature-based solutions utilizing the planting and rehabilitation of mangroves and seagrass beds are proposed. Sustainable green-engineered coastal protection strategies are pertinent for the low-lying coastal regions, as they house 70% of the country's population and roughly 80% of its socio-economic activity. Such measures offer ecological, environmental, social, and economic benefits, not provided by grey engineering, or concrete structures. Nature-based solutions are limited by anthropogenic factors, biotic/abiotic factors, data gaps, legal constraints, and social trends. These have resulted in declines in mangrove and seagrass bed coverage. A more sustainable coastal protection strategy using mangroves and seagrasses can be achieved by addressing these limitations and systematically utilizing various coastal ecological species. Building capacity, community building and outreach, and revising legal approaches and policing measures are necessary to maximize the benefits mangroves and seagrass beds offer as coastal protection measures.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".