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Record W7071861187

TRA-932: PLANNING FOR GOODS MOVEMENT: ONTARIO'S FREIGHT-SUPPORTIVE GUIDELINES & OFF PEAK DELIVERIES PILOT

2016· article· en· W7071861187 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2016
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsMinistry of TransportChristian ministryPlan (archaeology)Transportation planningLand useDemand managementOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

As communities grow and change it has become increasingly important to understand, plan and design for the movement of freight in order to maintain goods movement efficiency and the economic competitiveness of communities, while integrating and balancing the needs of other transportation system users and the compatibility of surrounding land uses. The Ontario Ministry of Transportation has developed Freight-Supportive Guidelines to assist municipalities, planners, engineers, developers and other practitioners in creating safe and efficient freight-supportive communities. The Guidelines provide land use planning, site design, road design and operational best practices, examples and implementation tools that are applicable to a wide range of communities and municipalities across Canada. Transportation demand management strategies can also be used to improve the efficiency of urban freight movement. During the Toronto 2015 Pan Am and Parapan Am Games, the Ontario Ministry of Transportation conducted a pilot to explore the potential of using off-peak deliveries as an urban freight transportation demand management strategy. The pilot allowed businesses and municipalities to explore the suitability and potential benefits and challenges of off-peak deliveries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.217
GPT teacher head0.309
Teacher spread0.092 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

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