Connecting the Environmental Management System and Environmental Impact Assessment for Pipeline Projects
Bibliographic record
Abstract
The National Energy Board of Canada (NEB) oversees all aspects, including environmental protection, of the construction and operation of hydrocarbon transmission pipelines under federal jurisdiction. The NEB’s regulatory approach is to minimize regulatory burden while maintaining a high standard of environmental protection. To achieve this, the NEB is working toward implementing a flexible, risk-based regulatory approach in which processes fit the scope and range of applications it receives. The NEB requires its regulated companies to develop and implement the equivalent of an environmental management system relating to all aspects of their business. In evaluating the companies’ compliance, the NEB conducts formal audits of these systems to ensure they are appropriately developed, maintained and implemented. The NEB has recently initiated changes to its regulatory processes to utilize companies’ management system information collected during the audits to enhance its application and assessment processes. This paper will discuss how concepts related to risk and management systems principles and information collected during an environmental management system audit can be integrated into a regulator’s environmental impact assessment for a proposed pipeline project. How knowledge and lessons learned are transferred through all stages of the pipeline life cycle will also be discussed.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".