Guidelines for Creating Safe and Efficient Freight-Supportive Communities
Bibliographic record
Abstract
As communities change, through growth and intensification, it will become increasingly important to consider the needs of the freight movement industry. Efficient and effective freight networks, integrated with freight-supportive developments, will help ensure that consumers and businesses have access to the goods and services they need and will help support local economies. Recognizing that goal, the Ontario Ministry of Transportation has produced this first-of-its-kind draft Freight-Supportive Guidelines. The purpose of the draft Guidelines is to help municipalities, planners, engineers, and other practitioners create safe and efficient freight-supportive communities. The draft Guidelines address planning, site design and transportation operations, and include strategies in each area to support freight movement. The draft Guidelines indicate how to conduct a freight audit to review existing supply of freight facilities and also measure the demand for these facilities. The freight audit is the first step in data collection and analysis that will help communities in their subsequent planning efforts for freight movement. The draft Guidelines also include case studies of best practices from Canada and international locations. This paper focuses on key guidelines for supporting freight within multimodal environments. It describes the freight audit process, and highlights best practices examples. This paper is in effect an executive summary of the complete draft Freight-Supportive Guidelines report, and is designed to enhance the reader’s understanding of how freight fits into a modern community as it works to achieve other objectives such as complete streets, congestion management and metropolitan mobility. As the Freight-Supportive Guidelines report has been prepared on behalf of the Ontario Ministry of Transportation, its intended audience is Ontario local municipalities, regions and counties, and private developers working in Ontario. While select guidelines may be Ontario-specific, the Freight-Supportive Guidelines provide pertinent information regarding land use and transportation planning, site design and roadway operations that could help create freight-supportive communities across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".