Testing the benefits of on-street and off-street rapid transit alignments: implications for Winnipeg's Southwest Rapid Transit Corridor
Bibliographic record
Abstract
First, I would like to thank my advisors. Dr. Sheri Blake whose dedication and guidance was crucial in making this project a success, Dr. Richard Milgrom for his constant tutelage over the years and Dr. Ian Hudson’s keen eye for research rigor. Along with practicum advisors, a thank you is well deserved by other faculty members who contributed my practicum project and my overall edification. I would also like to thank my friends, colleagues and the Faculty of Architecture administration who were all an important part of my time spent at the University of Manitoba. This project would not have been possible if it were not for interviewees and focus group participants, thank you for your contributions. Thanks to my family far and farther away for their motivation and support, especially my parents Angela and Richard – thanks for the edits! Finally, thanks to, Kristie Spencer for her excellent design prowess and patience With the uncertainty of future energy supplies and the impacts of global warming, rapid transit is becoming increasingly important as part of the transportation mix
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".