U.S.-Canada Border Planning: Facilitating Transportation Across the Northern Border
Bibliographic record
Abstract
Border transportation planning involves the development of goals, objectives, and strategies for moving people and goods across the U.S.–Canada border. FHWA leads multiple binational stakeholders to collaboratively create safe and effective cross-border transportation. FHWA also facilitates the development and maintenance of the surface transportation system along the U.S.–Canada border to address existing and anticipated demand for cross border travel and trade while working with federal, state, regional, and local agencies, international partners at Transport Canada, the private sector, and various stakeholders. The trade relationship between the U.S. and Canada is the largest in the world. In 2016, over 378,000 trucks, 661,000 personal vehicles, and 1.2 million vehicle passengers crossed through the 119 Land Ports of Entry (LPOEs) along the U.S.'s 5,525 miles of shared border with Canada.1,2\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".