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Record W6941421915 · doi:10.13023/ktc.rr.2023.07

Investigation of Illegal Weigh Station Bypassing

2023· report· en· W6941421915 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2023
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementLaw enforcementRevenueWarrantBest practiceState (computer science)

Abstract

fetched live from OpenAlex

This study recommends best practices to curb illegal weigh station bypassing by commercial motor vehicles (CMVs). Analysis of historical data collected by Kentucky State Police – Commercial Vehicle Enforcement (KSP-CVE) for 2017-2021 revealed that CMV drivers were charged with illegally bypassing weigh stations 2,616 times. Drivers were charged with an average of two other violations when cited for illegal bypassing — most often violations related to credentialing, vehicle safety, or driver safety. Site visits to three permanent weigh stations in Kentucky revealed that CMVs regularly bypass weigh stations illegally, including those authorized to use preclearance systems (Drivewyze and PrePass). A survey distributed to law enforcement officials in U.S. and Canadian jurisdictions found that 49% of the responding jurisdictions have seen a recent uptick in illegal bypasses. Most participating jurisdictions (70%) have conducted enforcement details to tamp down illegal bypassing, which indicates it is a widespread problem. Every illegal bypass likely results in jurisdictions missing out on revenues and increases the likelihood of poor safety outcomes. The safety and financial implications of illegal bypasses are substantial enough to warrant swift, comprehensive action to mitigate them.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.997

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)0.0050.000

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 head0.071
GPT teacher head0.240
Teacher spread0.170 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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