Aggregate Truck Queuing Time Relations at the Ambassador Bridge and Blue Water Bridge Border Crossing Facilities
Bibliographic record
Abstract
Relationships between truck queuing times immediately upstream of primary inspection stations and truck volumes and inspection times are estimated at the Ambassador Bridge and Blue Water Bridge international crossing facilities. The estimations are possible because of the recent availability of queuing and inspection time data obtained through the deployment of new technologies. Since truck volumes are available only at the monthly level, truck-level queuing and inspection time data are converted to monthly values, and aggregate relationships are estimated. Relationships are estimated for each crossing facility when using a set of 54 months of data and when dividing the data into subsets that represent a past and a recent period. Despite the aggregate nature of the data, strong relationships are produced that exhibit increased queuing times with increased monthly truck volumes and queuing time-to-volume elasticities greater than one. Differences in estimated elasticities, depending on crossing facility and direction, are consistent with different roadway characteristics upstream of the inspection stations. Relations exhibiting increased queuing times with increasing inspection times are produced, and the estimated coefficients are large enough to reflect the impacts on queuing times of differences in United States and Canadian inspection times. Changes in estimated relations from past to recent periods are consistent with infrastructure projects and improvements at the crossing facilities.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".