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Record W607792280

Variable Messaging for East-West Highway Corridors through Canada’s Mountain National Parks; Prospects for Multi-Agency Collaboration and Integration

2005· article· en· W607792280 on OpenAlexaboutno aff
Robyn McGregor, Alfred A Guebert, Terry McGuire

Bibliographic record

Venue12th World Congress on Intelligent Transport SystemsITS AmericaITS JapanERTICO · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDisseminationContext (archaeology)Agency (philosophy)Conceptual frameworkTransport engineeringGeographyEnvironmental resource managementEnvironmental planningEngineeringEnvironmental scienceTelecommunicationsArchaeologySociology
DOInot available

Abstract

fetched live from OpenAlex

Various methods of disseminating information about the weather and road conditions in the Mountain National Parks are already in use by Parks Canada, Alberta Infrastructure and Transportation and the British Columbia Ministry of Transport. Some of these methods utilize advanced technologies to disseminate variable messages. The Variable Messaging Conceptual Design for Mountain National parks was completed in 2004. This report provided a conceptual design and cost estimate for the implementation of variable messaging along major transportation corridors within and adjacent to the Mountain National Parks. This study has been extended to examine messaging needs from Vancouver through to the Alberta/Saskatchewan Border, along major highway corridors. There are already strong coordination efforts for operation of the transportation system and coordination of traveler information between Parks Canada and the provincial agencies. This paper will report on the results of this study and present the conceptual design and operational context for variable messaging along these corridors.

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)0.005
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.304
Teacher spread0.268 · 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
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
Published2005
Admission routes1
Has abstractyes

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Same venue12th World Congress on Intelligent Transport SystemsITS AmericaITS JapanERTICOSame topicTransportation Planning and OptimizationFrench-language works237,207