Redevelopment of Canada’s Second Busiest Border Crossing – An Exercise in Consensus Building
Bibliographic record
Abstract
This paper describes how Canada and the US have developed the largest bilateral trading partnership in the world over the past two decades. Commercial and passenger traffic crossing the border has increased so significantly that many land border operating authorities are forced to expand their facilities in order to accommodate the growth. The Canadian Authority for Blue Water Bridge (BWB) between Point Edward/Sarnia in Ontario and Port Huron in Michigan has recently completed a Master Plan addressing these growing demands and evolving border operating policies. The longer-term needs for the Plaza were identified through pro-active consultations with a multitude of stakeholders as well as a thorough assessment of the future traffic and infrastructure requirements. At the outset of the planning exercise, the study team identified a list of key objectives that the eventual plan had to meet. Sensitivity tests on future projected traffic and various processing rates were conducted in determining the processing infrastructure requirements, e.g. inspection and toll lines. Significant cooperation was fostered with a wide variety of stakeholders involving in inter-related issues such as border security traffic flow & safety, plaza operations, land exchange, cost sharing, neighborhood impacts, local access and tourism. Specific Plaza operational issues were identified and used as input to develop alternative plaza layouts which ultimately resulted in the development of the recommended plan creating an optimum balance in terms of meeting the requirements of the various users and stakeholders. Although Plazas at a border crossing have no standard layout or size, they all consist of similar infrastructure: toll (usually), duty free, primary inspection lanes, secondary inspection areas, administration & maintenance facilities, and ITS installation. In developing the BWB Canadian Plaza Master Plan, about twenty alternative plaza layouts were considered including many sub-options. These included layouts of primary and secondary inspection areas addressing various geometric challenges, as well as the location and configuration of buildings and parking areas. A screening assessment of the long list of alternatives led to a detailed analysis of a short listing of four alternatives and the selection of a preferred layout. The Plan included a phasing plan and a construction staging plan in which the key challenge is maintaining operations and traffic on a 24/7 basis.
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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 | 0.139 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| 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".