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Record W578209612

CVSA AND MCSAP: A FEDERAL-STATE PARTNERSHIP

2004· article· en· W578209612 on OpenAlexaboutno aff
Stephen A Keppler

Bibliographic record

VenueJournal of transportation law, logistics, and policy · 2004
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementCertificationGeneral partnershipBusinessMotor carrierLaw enforcementTransport engineeringCrashService (business)Computer securityEngineeringFinanceLawPolitical scienceComputer scienceMarketing
DOInot available

Abstract

fetched live from OpenAlex

The Commercial Vehicle Safety Alliance (CVSA), an association of state, provincial and federal officials is responsible for the administration and enforcement of motor carrier safety laws in the United States, Canada and Mexico. Its mission is accomplished through establishment of effective motor carrier, driver, vehicle and cargo safety standards along with compliance, education, training and enforcement activities. The Motor Carrier Safety Assistance Program (MCSAP), created to improve motor carrier safety and reduce commercial vehicle crashes, is another example of a federal aid program that has worked. As a result of the partnership between CVSA and MCSAP, the following has been accomplished: North American standard roadside inspection procedures; North American standard out-of-service criteria; out-of-service and other defect repair verification procedures; complaint control procedures; uniform maximum firm schedules; inspector and instructor training, certification and recertification practices and procedures; safety information, software and data systems; and performance of roadside inspections, traffic enforcement, compliance reviews and crash and incident investigations.

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)0.008
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.001
Scholarly communication0.0070.002
Open science0.0020.008
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0340.005

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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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
GenreOther

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
Published2004
Admission routes1
Has abstractyes

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