Understanding freight for highway engineering and planning
Bibliographic record
Abstract
How would potential changes in truck size and weight policy impact truck flows?Possible Manitoba examples include: banning cefain RTAC routes in spring periods; specially-permitted long combination vehicle operations on undivided highways without paved shoulders (Clayton et aI.,2003); authorizing modifications to existing RTAC truck size and weight (TS&W) regulations (e.g., lift axles, split tandems, wide-based tires, long semitrailers, short/long wheelbase tractors); rationalizing and harmonizing seasonal weight limits.o How would transportation network developments in adjacent jurisdictions impact provincial highway trucking?For example, how would extensions of the Chief Peguis Trail in North Wiruripeg modify truck flows on the North Perimeter Highway? o How would transportation network modifications associated with the Winnipeg Floodway expansion affect network use, major shippers, and transport efficiency to/from Winnipeg? .What commoditymovements, industrial sectors and communities would be affected, and to what extent, by changes in seasonal weight limits goveming trucking operations due to possible climate change scenarios (e.g.shortening of the winter weight premium period)?These and other transportation engineering and planning issues require more objective capabilities to understand and forecast freight and related truck movements on provincial highways in Manitoba and elsewhere.This research is directed at helping to develop these capabilities.1.3 OBJECTIVES AND SCOPE Specific objectives ofthe research are:1. To identify and assess readily available freight data sources and examples of methodologies for utilizing such data for highway engineering and planning. To develop a GIS-T platform for the research.3. To discuss, define, and charactenze key aspects of the Manitoba freight system relevant to the research.These are: vehicles, freight characteristics, the demand system, industry perspectives, and the truck planning network as part of the general highway system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".