A land-based oil spill management planning framework for the petroleum industry
Bibliographic record
Abstract
Oil spill pollution efforts have historically focused on ocean-based oil spills and response has typically been reactive. Strategies to regulate and mitigate oil spills occur at all levels, internationally, nationally, and at the industry level. Absent is a strategy geared specifically to manage and mitigate land-based oil spills by the petroleum industry. To address this need, the objective of this study proposes a comprehensive oil spill management planning framework. It is comprised of five components, oil spill prevention, control, clean-up, and emphasis on the characterization and economic evaluation of oil spills. Land-based oil spills is a significant pollution problem largely due to a decaying pipeline infrastructure, which have escalated oil spill costs, volumes and frequencies. Using oil spill data collected at a petroleum company in Trinidad, statistical analyses and structural and non-structural concepts, adapted from Ontario's flood management approach, are applied to understand and mitigate oil spill events and costs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".