Development of a Strategic Goods Movement Network in Peel Region
Bibliographic record
Abstract
The Region of Peel is Canada's largest multi-modal freight hub. Located just west of Toronto, Peel is home to nationally important freight facilities, notably Pearson International Airport, intermodal rail terminals and several expressways. Peel also has some of the country's largest manufacturing and assembly plants and distribution centres. Given the importance of its goods-generating infrastructure and industry to the regional and national economies, Peel has championed a public-private goods movement Task Force, which aims to coordinate and improve the efficiency and interconnectedness of the region's multi-modal goods movement network. In support of these aims, in early 2012 the Task Force identified 23 go-forward actions. One of the first of these actions was to develop a region-wide strategic goods movement network (SGMN), which in turn will serve as the framework for coordinating and prioritizing other actions. The SGMN is a holistic approach to developing a goods movement network in a systematic way. The final concept plan respects planning policies (e.g., avoiding where possible routes through residential neighbourhoods and reliance upon proposed BRT/LRT corridors) while promoting direct connectivity and accessibility to goods-generating activity centres. The SGMN concept - essentially a map - is supported recommended 'next step' implementation actions to actualize the network. Together with the criteria and performance indicators, these provide both the basis for implementing the network now and the 'rules' for future updates. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".