A Spatial Multi-Criteria Analysis Process to Optimize and Better Defend the Pipeline Route Selection Process
Bibliographic record
Abstract
Linear infrastructure routing experts struggle with providing rationale for final route selections that are defensible, transparent, open to two-way stakeholder communication, and ultimately scientifically rigorous and repeatable. Likewise, proponents are wise to adopt a front-end, risk-based approach to proactively identify, mitigate or possibly avoid routing decisions that may result in stakeholder opposition and costly permitting and/or approval delays. This paper discusses an approach using Geographic Information Systems (GIS) and a highly structured multi-criteria spatial analysis (MCA) to identify potentially optimal corridors and routes based on best available environmental, social, economic and technical spatial datasets. This approach can be used by multidisciplinary project teams to systematically capture, explore and record routing protocols and assumptions, and then extrapolate these considerations through GIS modelling into simulated corridor and route options which can then be quantitatively analyzed, compared, documented and communicated. Early identification and mitigation of project routing risks may help reduce or avoid costly project delays at later stages. Stakeholder communication and consultation can be incorporated at each stage in order to inform routes and explore trade-offs, as well as communicate routing rationale in an open, constructive and meaningful way. The resulting benefits of this approach include a robust and comprehensive rationale, providing proponents with a clear and compelling “story” in support of public and stakeholder consultation as well as the regulatory approval process.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".