Bibliographic record
Abstract
Saskatoon, like many cities, is attempting to diversify the transportation options available to its citizens and take advantage of the benefits that urban cycling provides through an increased investment in bicycle infrastructure. The recent development of a bicycle boulevard, a street that gives priority to cyclists over motor traffic, is an example of Saskatoon’s shift. This study examines the effectiveness of Saskatoon’s bicycle boulevard by taking an inventory of bicycle infrastructure on this route, measuring demographic information of boulevard users, performing traffic counts, and conducting intercept surveys of boulevard users. This data was analyzed through the lens of National Association of City Transportation Officials (NACTO) boulevard design standards. The data gathered from these methods indicates that Saskatoon’s boulevard is successful in some areas and needing improvement in others. While the boulevard appears to be successfully address design considerations relating to signage/pavement markings, route planning, and traffic speeds, the City has been less successful in managing traffic volumes and dealing with areas where cyclists must compete with motorists for road space. This study recommends that Saskatoon install motorist volume control measures at key areas of perceived danger throughout the boulevard to increase its effectiveness. Repaving the street and tackling perceptions of crime in the area were additional recommendations based on the findings of this study. AcknowledgementsThis project owes could not have happened without the guidance of Dr. Jill Gunn in the Department of Geography and Planning at the University of Saskatchewan. Her knowledge of field research techniques formed the basis of this report. Special thanks must also be extended to Sean Shaw of Saskatoon Cycles for introducing the author to the study site.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".