Risk‐based Approach to Designing and Reviewing Pipeline Stream Crossings to Minimize Impacts to Aquatic Habitats and Species
Bibliographic record
Abstract
Abstract Extensive new pipeline systems proposed to transport natural gas and oil throughout North America will potentially result in thousands of new stream crossings. The watercourses encountered at these crossings will range from small, ephemeral headwater streams to large, perennial mainstem rivers; from dynamic gravel‐bed streams to stable bedrock channels; and from steep, source reaches to low gradient, response reaches. Based on past experience at pipeline crossings, the potential for both short and long‐term negative impacts on aquatic habitat and species is substantial. In assessing potential hazards to aquatic habitat and species, the diverse physiography and ecology of the stream affected, combined with the number and range of new pipelines proposed, pose significant challenges for project developers charged with collecting, stratifying, evaluating, analysing, interpreting, and presenting stream crossing data in formats that are accessible, usable and useful. It is equally challenging for project reviewers to detect, distill and summarize potential project impacts and then identify reasonable options for their avoidance, minimization, and mitigation. To address these concerns, the US Fish and Wildlife Service, in conjunction with Ruby Pipeline, LLC, developed a pipeline crossing framework and risk analysis approach to stratify potential aquatic impacts, based on both stream characteristics and project types. In this approach, pipeline crossings are ranked in terms of relative short and long‐term risk to aquatic habitat and are then analysed, designed, and monitored in ways appropriate to their risk. This approach allows project developers and reviewers to focus resources and monitoring on the crossings that present the highest risks to aquatic habitat and species, while expediting design and construction, and minimizing the monitoring of low‐risk crossings. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.102 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".