Systematic Process to Develop a Strategic Goods Movement Network in Peel Region, Canada
Bibliographic record
Abstract
The Region of Peel, in Ontario, Canada, one of Canada’s most important concentrations of multimodal goods movement hubs and goods-generating industries, conducted a Strategic Goods Movement Network (SGMN) study during 2012 and 2013. The paper describes how the study team, guided by SGMN best practices, created the region’s first SGMN and successfully balanced the importance of facilitating efficient goods mobility with the Region’s goals for smart growth, quality of life and economic vitality. To achieve this balance, using visual treatments the study integrated public and private stakeholder input with truck movement data, geographic information system (GIS) layers and modeling outputs. Despite the disparate cross-section of agencies and individuals involved with reviewing and approving the study recommendations, the technical approach used by the research team conveyed the study’s results and proposals in an efficient, compelling, and balanced manner. The final strategy proposes an SGMN founded on a hierarchy of goods movement routes that optimizes the different types of truck movements serving the Region. The route hierarchy emphasizes directness, continuity, connectivity, and reliability for trucking operations. The SGMN is also designed to support Peel Region’s quality of life-oriented planning and development policies, thereby facilitating continuing efforts to achieve “peaceful coexistence” of both the goods movement industry and the Region’s residents and businesses. While this paper focuses on Peel, much of what was experienced is transferable to other areas facing similar goods movement challenges.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".