Using Strategic Environmental Assessments to Guide Oil and Gas Exploration Decisions in the Beaufort Sea: Lessons Learned from Atlantic Canada
Bibliographic record
Abstract
The 21st century has seen a renewed interest in developing Canadian Arctic oil and gas reserves. Historically, hydrocarbon development efforts focused on land or shallow water hydrocarbon potential. Since 2008 the industry has shifted its attention to the deepwater areas of the Canadian Beaufort Sea — a region that to date has experienced limited exploration and no development. In the wake of the huge Macondo oil spill in the Gulf of Mexico, Canada's National Energy Board (NEB) initiated a public Review of Offshore Drilling in the Canadian Arctic to ensure the regulatory system was prepared to handle the unique challenges of Arctic drilling. There was no similar examination of the adequacy and appropriateness of Canada's Arctic oil and gas rights issuance process. In this paper we argue that a key weakness in the current procedure is the failure of the government to apply state of the art Strategic Environmental Assessments (SEAs) as part of deciding where and when to open new areas to potential oil and gas drilling activities.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".