Parallel or Converging? A Comparative Analysis of the Grain Handling and Rail Transportation Systems in Canada and the United States (Summary, February 2021
Bibliographic record
Abstract
Railroads are the predominant mode for moving heavy freight long distances in North America.In both the United States and Canada, railroads face the same basic operational fundamentals to move freight from an origin to a destination.Likewise, in both countries, the industry is capital-intensive and characterized by few firms, making the regulator's task a difficult one.Rail regulators-the Surface Transportation Board (STB) in the United States and the Canadian Transportation Agency (CTA)-must balance competing needs.Most notably, they must protect railroads' ability to earn adequate revenues to invest in infrastructure while protecting shippers and end consumers from abuses of market power.Yet, despite their similarities, railroads in each country operate in very different regulatory environments.This is a product of different geographies, historical developments, shipper pressures, policymaker philosophies, and other factors.In recent years, regulators in both countries have increasingly looked across the border for insight and ways to improve outcomes for railroads and shippers.For example, Canadian railroads have pushed the CTA to eliminate the revenue cap over grain shipments (a feature not present in the United States), and STB has considered implementing a new rate review process that includes a "final offer" component, a key feature of rate review in Canada.These regulatory elements and others are described in the next section.This study compares, contrasts, and evaluates the U.S. and Canadian rail systems, with an emphasis on grain transportation.It describes the evolution and recent history of key rail regulations in each country and highlights key operational differences.
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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 | 0.005 | 0.016 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".