THE APPLICATION OF OPTION MODELING TO RAIL ABANDONMENT POLICY DECISIONS IN CANADA
Bibliographic record
Abstract
The liberalization of Canadian railroad industry began in 1967 with the passage of the first National Transportation Act, but the passage of the Canada Transportation Act (CTA) in 1987 meant that Canadian Class I railroads were given increased freedom to abandon unprofitable branch lines in order to consolidate their network operations. From 1984 to 1996 the total mileage of branch lines fell about 14%, most of this decline occurring after the enactment of the CTA in 1987. This paper examines the methods used by the CTA to evaluate rail abandonment decisions. Modern financial pricing models have been constructed to improve the cost benefit analysis by incorporating explicit valuation of risk into the analysis. The paper argues that by applying real options modeling to the rail abandonment decision framework, the decision process will become more transparent and will be better able to incorporate any potential future uses of track scheduled for abandonment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".