Understanding and Promoting Multimodal Freight Transportation System Performance within Mega-Regions: Lessons from Great Lakes-Saint Lawrence Basin
Bibliographic record
Abstract
The Great Lakes Saint-Lawrence Basin (GLSLB) is a bi-national economic mega-region. It generates a disproportionate share of U.S. and Canadian economic activity and trade relative to its size and is home to a significant share of the two countries’ population. The region’s multimodal freight transportation system is essential to the economy of the region and beyond. The complexity of the GLSLB freight transportation system cannot be overstated. It spans all transportation modes, several jurisdictions, and handles a range of commodities, each with different transportation requirements. The authors are part of a team undertaking a study under the Transportation Research Board’s National Cooperative Freight Research Program (NCFRP) to describe the current multimodal freight transportation system within the GLSLB. This paper summarizes some of the key findings of this work. It explores the characteristics of this regional freight transportation system and its economic importance. This paper also explores the barriers and constraints to the effective performance of this system and the opportunities to improve it, particularly through the development of an effective model for future research, planning and policy development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".