Case study analysis of factors influencing the adoption of heavy axle loading on Canadian short-line railroads
Bibliographic record
Abstract
The purpose of this research is to analyze factors influencing the adoption of heavy axle loading (HAL) on short-line railroads in Canada. The research comprises a series of case studies which characterize selected Canadian short-line railroads. The approach stratifies the industry in terms of the type of ownership and geographic region. It also documents factors influencing the adoption of HAL for each railroad by examining the commodities it hauls (internal motivation) and its network connections to the Class 1 system (external motivation). Where available, infrastructure condition data are also reported. These studies revealed that all of the 31 railroads studied exhibited a medium-high or high overall motivation to adopt HAL. This reflects the nature of the short-line industry in Canada—filling a niche by hauling primarily heavy commodities (thereby providing internal motivation) while relying on Class 1 partners to offer complete services to their customers (thereby providing external motivation).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".