Modelling Cross-Border Rail Intermodality in the Windsor-Essex Context
Bibliographic record
Abstract
Shipment by truck dominates the cross-border flow of commodities in both directions between Canada and the United States (Anderson and Coates, 2010; Anderson, 2012; Anderson and Brown, 2012; and Aspila and Maoh, 2014). An individual truck typically pulling one or two trailers is an inefficient way to move goods over long distances (Eom et al., 2012) when freight trains with three or more 4400 horsepower diesel-electric locomotives pull over two-hundred intermodal containers loaded on rail cars throughout North America every day. Windsor, Ontario is an example of a border community in Canada and hosts the busiest border crossing between Canada and the United States. Crossings include two road, one rail and a sea port of entry (United States Department of Transportation – Bureau of Transportation Statistics, 2017). Presently the majority of cross-border import and export traffic is by road haulage. In addition to serving as a port of entry for goods being imported or exported between the two countries there is also a substantial local manufacturing base that consumes and produces goods on both sides of the border. There are several existing railroad border crossings including a rail tunnel between Windsor, Ontario and Detroit, Michigan. There must be a rational reason why commodities are shipped across the border using trucks and not rail. This dissertation research is proposed to answer the question of is rail viable for shipping commodities cross-border or as part of the cross-border supply chains? A network optimization model of Canada-US rail freight is developed to address this question. The model is first used to assess whether location of a conventional, large-scale intermodal facility in Windsor is viable. Results indicate that it is not. It is then applied to a scenario where innovative small-scale intermodal transfer facilities are located in Windsor and at other significant rail nodes in Ontario. Results indicate that this is a more viable strategy for increasing the rail share of cross-border freight movement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".