Economic use value of the <scp>B</scp>elize marine ecosystem: Potential risks and benefits from offshore oil exploration
Bibliographic record
Abstract
Abstract The announcement of plans for exploratory oil drilling at a number of offshore sites in Belize raised concerns about the risks associated with drilling, particularly given the socio‐economic importance of the marine ecosystem. The current economic value of fisheries and marine ecotourism is estimated, along with the potential revenue from offshore oil and potential economic losses stemming from oil pollution, under various assumptions on risk and uncertainty. Marine fisheries and ecotourism are estimated to generate around US$ 183 million per year. Single‐year estimated maximum revenue is higher for oil extraction initially but quickly declines; during a 50 year (two generation) period, total discounted benefits from marine fisheries and ecotourism are estimated at US$ 5.1 billion, compared to US$ 3.2 billion from offshore oil revenue. Following a hypothetical oil spill, discounted losses in marine fisheries and ecotourism due to perception and ecological impacts are estimated at US$ 912 million, with clean‐up costs and capital losses of US$ 6.1‐10.4 billion. Considering the short extraction life of oil resources compared to fisheries and ecotourism, the difference in benefits increases substantially in favour of the latter with a longer time horizon. A recent public referendum resulted in a 98% vote against oil exploration and a subsequent annulment of oil concessions pending environmental impact assessments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".