Regulatory Compliance on Multistate and Multimodal Projects: Bridging the Gaps Between States and Among NEPA Co-leads
Bibliographic record
Abstract
The I-5 Columbia River Crossing (CRC) project is a highway and transit project located on Interstate 5 (I-5) along a five mile corridor between Vancouver, Washington and Portland, Oregon. Spanning two states, cities and counties, the CRC project has many different jurisdictional boundaries that can include different ideologies, requirements, and established practices. Two Metropolitan Planning Organizations and transit organizations also play a primary role for the transit side. In total, the project has eight project sponsors, including the Oregon and Washington Departments of Transportation. The project includes both major highway and major transit elements, and therefore two federal lead agencies – the Federal Highway Administration (FHWA) and the Federal Transit Administration (FTA) – jointly oversee the National Environmental Policy Act (NEPA) process. The federal co-lead status for developing the Environmental Impact Statement (EIS) often presents challenges that will be discussed. On the regulatory side there are obvious complications from the bi-state nature of the project because each state has its own regulations and policies that can be inconsistent and sometimes contradictory to the other. The purpose of this paper is to explain the CRC approach to environmental streamlining for the NEPA process and the lessons learned that could apply to complex or even smaller transportation projects
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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 | 0.080 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".