Quebec-Windsor Corridor High Speed Rail Market Forecast Profiles in Context: Level-of-Service Response Curvature Sensitivity and Attitude to Risk or to Distance in Forty Logit Core Model Applications of the Law of Demand
Bibliographic record
Abstract
Correspondence address: marc.gaudry@umontreal.ca. This paper, originally entitled “Non Linear Logit Modelling Developments and High Speed Rail Profitability” in July 2008, differs substantially from that first version due to a decision, prompted notably by Andrew Daly, to expand a section summarizing results obtained in Logit models making use of Box-Cox transformations. The expanded Survey, found is Section 5, is based on collaborations with co-authors of the joint papers, past or in progress, plundered for the Survey, notably Staffan Algers, Florian Heinitz, Matthieu de Lapparent, Jorg Last, Alexandre Le Leyzour, Benedikt Mandel, Werner Rothengatter, Cong-Liem Tran and Michael Wills. Such international research would have been impossible without the support over the years of Transport Canada, of the National Sciences and Engineering Council of Canada (NSERCC), of the
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".