OFFSHORE HYDROCARBON AND SYNTHETIC HYDROCARBON SPILLS IN EASTERN CANADA: THE ISSUE OF FOLLOW-UP AND EXPERIENCE
Bibliographic record
Abstract
The Environmental Assessment (EA) process should involve the generation of testable predictions generated using clearly stated methods and followed by the collection of environmental monitoring data. Follow-up programs should aim to determine the accuracy of the initial predictions. We examined the follow-up process for six oil and gas extraction projects in eastern Canada with respect to assessing batch spill (< 50 barrels of hydrocarbons and synthetic hydrocarbons) predictions. For three projects we compared oil spill frequency predictions to observed data. All three projects exceeded their predicted frequencies and two projects by ratios (actual to predicted) greater than six. Spill histories from earlier projects, clearly exceeding predictions of future projects, are not provided in subsequent oil and gas EAs for the region, when there were opportunities to do so. We provide recommendations on how to strengthen the quality of EAs and increase protection of the marine environment in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".