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Record W813784564

Analysis of U.S. Truck Size and Weight Policy in Relation to Vehicle Mass

2012· article· en· W813784564 on OpenAlexaboutno aff
John Woodrooffe, Dan Middleton

Bibliographic record

VenueTransportation Research Board 91st Annual MeetingTransportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsTruckProductivityFuel efficiencyAxleInefficiencyTransport engineeringArticulated vehicleTractorEngineeringAgricultural economicsAutomotive engineeringBusinessEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Truck transportation efficiency including fuel consumption and greenhouse gas production per unit of freight transported is directly influenced by truck size and weight policy. This paper compares the cargo mass productivity of U.S. workhorse articulated vehicles with those from other developed nations in relation to allowable axle loads and GVW. The U.S. Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 limited the gross vehicle weight to 80,000 lbs which has constrained vehicle mass to the point that within the NAFTA region, the U.S. vehicle mass productivity deficit for tractor semitrailers is 43% compared with Canada and 52% compared with Mexico. The paper provides a summary of the history of U.S. truck size and weight policy and concludes with the following questions. 1) Is the U.S. infrastructure strength significantly weaker than that of other developed countries? 2) What is the cost to the nation, to the environment and energy supply for the apparent lack of transportation efficiency? 3) Why does the U.S. not have an integrated national transportation policy to optimize transportation efficiency? 4) Have lower axle and gross vehicle weights resulted in fewer fatalities or has the greater vehicle exposure attributed to transportation inefficiency resulted in more fatalities?

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)0.004
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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
Published2012
Admission routes1
Has abstractyes

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