Characterization of International Origin–Destination Truck Movements Across Two Major U.S.–Canadian Border Crossings
Bibliographic record
Abstract
In 2013, 3.9 million trucks crossed between the United States and Canada at the Ambassador Bridge or the Blue Water Bridge. These trucks accounted for 43.1%, by value, of all truck trade between the two countries. GPS pings that tracked the movement of Canadian-owned trucks over a span of 1 year were used to characterize freight activities at the two Canada–United States border crossings. (A “ping” is a GPS data record that identifies the location of a subject at a given point in time. The term is commonly used in the information technology and communications industries.) A total of 172,000 and 82,000 crossing events were identified and analyzed at the Ambassador Bridge and the Blue Water Bridge, respectively. This paper describes the development of origin–destination data pertaining to truck trips that utilize the two border locations. The paper also includes an estimation of the industries involved in individual trips, on the basis of those trips’ start and end locations. The combination of origin, destination, crossing time and location, and industry provides an immense amount of information on the nature of international truck movements at the Canada–United States border. The resulting spatial patterns provide evidence that the two border crossings are used for both short- and long-range trips that include locations on the West Coast and the southern U.S. border. Although crossing times did not vary considerably by industry, they were influenced by the distance of the trip: short-distance journeys had shorter crossing durations, on average.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".