GIS-Based Material Stock Analysis (MSA) of Climate Vulnerabilities to the Tourism Industry in Antigua and Barbuda
Bibliographic record
Abstract
In the past decades, the Caribbean economy has transformed to rely primarily on tourism with a vast amount of infrastructure dedicated to this sector. At the same time, the region is subject to repeated crises in the form of extreme weather events that are becoming more frequent, deadly, and costly. Damages to buildings and infrastructure (or the material stocks) from storms disrupt the local economy by an immediate decline in tourists and loss of critical services. In Antigua and Barbuda (A&B), tourism contributes 80% to the GDP and is a major driver for adding new material stocks to support the industry. This research analyzes A&B’s material stocks (MSs) in buildings (aggregates, timber, concrete, and steel) using geographic information systems (GIS) with physical parameters such as building size and footprint, material intensity, and the number of floors. In 2004, the total MSs of buildings was estimated at 4.7 million tonnes (mt), equivalent to 58.5 tonnes per capita, with the share of non-metallic minerals to be highest (2.9 mt), followed by aggregates (1.2 mt), steel (0.44 mt), and timber (0.18 mt). Under the National Oceanic and Atmospheric Administration’s (NOAA’s) 2 meter (m) sea level rise scenario, an estimated 4% of the island’s total MSs would be exposed. The tourism sector would disproportionately experience the greatest exposure of 19% of its MSs. By linking stocks to services, our research contributes to the understanding of the complexities between the environmental and economic vulnerability of island systems, and the need for better infrastructure planning as part of resilience building.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".