Vulnerability assessment survey of oil and gas facilities to climate-driven sea level rises and storm surges on the west coast of Trinidad
Bibliographic record
Abstract
Greenhouse gas (GHG) climate change/global warming is one of the most pressing environmental concerns today.Small Island States, such as Trinidad and Tobago, are highly vulnerable to climate change because of their small size and low elevation, as in the case of this study, which increases their sensitivity to climate change and limits their ability to adapt.In fact, the adaptive capacity of human systems is generally low in Small Island States, and vulnerability generally high.The Petroleum Company of Trinidad and Tobago (PETROTRIN) recently conducted a detailed vulnerability assessment survey and storm surge simulation through modelling for the west coast of Trinidad stretching from Vessigny River in the North to Cap-de-Ville in the South along the Gulf of Paria.This survey was undertaken so as to identify the impacts of climate driven, sea level rises and extreme storm surge events on Petrotrin's and Trinmar's infrastructure and operations.The methodology used to conduct this vulnerability assessment survey involved coupling A-OGCM (Atmosphere-Ocean General Circulation) model simulations of future sea level rises and TAOS (Total Arbiter of Storms) estimates of storm surges to a GIS-based inundation and erosion scheme so as to estimate land loss and infrastructure facilities at risk from inundation and erosion.The results of the study show that field installations in Petrotrin at Guapo, such as access roads, pipelines, storage tanks and even pump jacks and the offshore operations of Trinmar including offshore platforms, jetties and harbours and administrative buildings would be at severe risk of inundation and erosion deriving from sea level rises and storm surge events.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".