A Comparison Of Environmental Assessment (EA) Prediction Practices For Offshore Oil And Gas In Canada And Nigeria: How Do They Compare To Best Practices In EA Literature In Relation To Seabirds And Marine Vertebrates?
Bibliographic record
Abstract
Anthropogenic economic activities are progressively harming the ocean environment. This is true of the oil and gas sector, which has increased in scale, and is a major driver of the offshore economy. Oceans are severally polluted, as a result, through vessels accidents, accidental spills and large oil spill. There is also the challenge posed by seismic activities and operational installations associated with offshore oil and gas projects. Evidently, offshore oil and gas operations levy extensive impacts on seabirds and marine vertebrates, and the totality of the marine environment. The goal of Environment Assessment (EA) is to predict project environmental impacts with a reasonable degree of certainty. In the offshore oil and gas sector of most jurisdictions, EA is a compulsory requirement for project approvals. This paper considered the EA prediction practices of Canada and Nigeria. In the process, the Environmental Impact Statements (EIS) of the Terra Nova and Hebron offshore oil projects in Newfoundland and Labrador, Canada and the Diebu Creek and Jones Creek Nearshore oil projects of the Niger-Delta of Nigeria, were analyzed and compared. The objective was to investigate the EA prediction processes of these two countries and how they met best practices, in relation to predictions on seabirds and turtles. The paper concludes with a critical evaluation of the performance of the sampled EIS documents. The outcome of the analysis indicated a weaker EA prediction regime in Nigeria. The Canadian counterpart appeared stronger in its adaptation to best practices, although there are gaps in the process, suggesting a necessity for improvement.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".