Cross Border Transportation Patterns at the Western Cascade Gateway and Trade Corridor: Implications for Mitigating the Impact of Delay on Regional Supply Chains
Bibliographic record
Abstract
This report presents a commercial vehicle profile of transportation patterns and a commodity profile of the primary border crossing along the Western Cascade border region of southwest British Columbia, Canada, and northwest Washington, United States, in particular the corridor between the urban areas of Vancouver, British Columbia, and Seattle, Washington. Because of the larger trade volumes along the eastern portion of the U.S./Canadian border between Michigan, New York, and Ontario, trade research on the northern U.S. border has typically focused on trade along the eastern portion of the border between Michigan, New York, and Ontario, as well as on immigration and customs issues along the southern border with Mexico. As a result, less attention has been given to the western portion of the U.S./Canada border. This research begins to fill that gap with both a description of regional trade and a description of current delay patterns, consequences of delay, and causes of delay. Using four data sources for comparison—a Global Positioning System (GPS) freight carrier border delay data set, a commercial volume data set (BC MoT), a detailed border operations survey data set and manifest sampling (WCOG)—the authors consider the linkages among volume, delay, border operations, commercial vehicle origin/destination, and commodities carried to create a commercial vehicle profile at the Cascade Gateway. The data also allow the authors to demonstrate transportation patterns at this gateway and along the trade corridor, and to show that they are very regional in nature. This research will benefit both public and private stakeholders who are interested in understanding cross border commercial vehicle commodity flows and transportation patterns in the Cascade gateway and trade corridor, as well as the profile of delay experienced at the Pacific Highway commercial border crossing. Such an understanding can aid in the development of solutions to mitigate border delay and its impacts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".