Bibliographic record
Abstract
This paper considers whether there has been any change in merchandise rail mode split along the Quebec-New York corridor since the terrorist attacks of September 11th, 2001. In particular the papers seeks to establish whether increased border security has led to increases in border wait times and whether border wait times have been affected differentially for trucks and trains providing competitive advantage to rail as a mode to move freight along this corridor. While it was not possible to compare actual border wait times before and after September 11th because of the fact that border wait time information was only collected after September 11th, it does not seem that there has been an appreciable increase in border wait time along this corridor and in particular not enough to provide an increased advantage to rail. As well, there is little support for the hypothesis that rail mode split was positively affected in the post 9/11 period. This includes traditional, bulk freight categories, as well as non-traditional freight categories, yet further econometric analysis is necessary to better establish this. However, it does seem that there has been a reversal in the pattern of declining rail mode split of imports from Quebec to New York starting at the beginning of 2001 – long before the events of September 11th. The actual reason for this reversal in the fortune of rail is not entirely understood and is the subject of continuing research. Patterson, Haider & Ewing 2
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.841 | 0.661 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".