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Record W7043951228

Understanding freight for highway engineering and planning

2007· dissertation· en· W7043951228 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2007
Typedissertation
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsTruckScope (computer science)Variety (cybernetics)Highway engineeringTraffic flow (computer networking)Information systemTraffic management
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.054
GPT teacher head0.204
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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