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Record W1978046658 · doi:10.1139/l01-073

Seasonal weight limits on prairie region highways: opportunities for rationalization and harmonization

2002· article· en· W1978046658 on OpenAlexvenueaboutno aff
Jeannette Montufar, Alan Clayton

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsRationalization (economics)HarmonizationEnforcementTruckGeographyBusinessAgricultural economicsEnvironmental protectionEngineeringPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

There are a myriad of laws, regulations, and policies governing the operating weights and dimensions of trucks. In Canada and many northern states, these regulations form a continuum of basic limits, seasonal variations, and overweight/overdimension limits that are legally permitted. This paper deals with the seasonal aspects of weight limits (winter weight premiums and spring weight restrictions) governing trucking within and to and from the prairie region. This region encompasses Manitoba, Saskatchewan, and Alberta, and the northern tier states of Minnesota, North Dakota, and Montana. The paper presents results of research conducted for the transportation departments of the three prairie provinces and Public Works and Government Services Canada. It discusses existing winter weight premium and spring weight restriction regulations, as well as basic weight regulations in the region, and their technical rationale. It examines possibilities for using advanced technologies to help harmonize and rationalize seasonal truck size and weight regulations and enforcement practices, and identifies immediate opportunities for rationalization and harmonization of spring weight restrictions and winter weight premiums.Key words: trucking, weight regulations, spring restrictions, winter premiums, prairie region, harmonization, rationalization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.060
GPT teacher head0.208
Teacher spread0.148 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2002
Admission routes2
Has abstractyes

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