Analysis and Transfer of Commodity-Specific Shortlines in Western Canada - Case Study: CN Avonlea Subdivision
Bibliographic record
Abstract
A brief overview of the transfer vehicles available to Class 1 railways is first presented.This is followed by a description of the financial and related qualitative factors considered by Canadian National Railways (CN) during the course of a potential shortline transfer.The method by which CN evaluates potential shortline partners is then presented.Conditions specific to the analysis of the Avonlea Subdivision are discussed.Part 1: HISTORICAL 1.Brief history of shortlining Shortlines may be described as generally short segments of lighter density railway whose primary function is to act as a feeder to main line, higher density carriers.'American Class 1 railroads began network rationalization efforts approximately 15 years ago.Today there are about 500 shortlines operating on 20 percent of the freight track in the United States.Successful shortlines are notable for their greater total assets, higher operating revenues, higher traffic densities and greater length of track operated.'Several regional and terminal railroads have operated in Canada for many years, spread out nationwide and carrying a wide variety of commodities including grain, chemicals, coal, forest products, intermodal and manufactured products.'However the creation of newer shortlines in Canada had advanced at a relatively slow pace since the introduction of the National Transportation Act (NTA) in 1987.This may possibly be due
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".