TRA-932: PLANNING FOR GOODS MOVEMENT: ONTARIO'S FREIGHT-SUPPORTIVE GUIDELINES & OFF PEAK DELIVERIES PILOT
Bibliographic record
Abstract
As communities grow and change it has become increasingly important to understand, plan and design for the movement of freight in order to maintain goods movement efficiency and the economic competitiveness of communities, while integrating and balancing the needs of other transportation system users and the compatibility of surrounding land uses. The Ontario Ministry of Transportation has developed Freight-Supportive Guidelines to assist municipalities, planners, engineers, developers and other practitioners in creating safe and efficient freight-supportive communities. The Guidelines provide land use planning, site design, road design and operational best practices, examples and implementation tools that are applicable to a wide range of communities and municipalities across Canada. Transportation demand management strategies can also be used to improve the efficiency of urban freight movement. During the Toronto 2015 Pan Am and Parapan Am Games, the Ontario Ministry of Transportation conducted a pilot to explore the potential of using off-peak deliveries as an urban freight transportation demand management strategy. The pilot allowed businesses and municipalities to explore the suitability and potential benefits and challenges of off-peak deliveries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".