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Record W4393066833 · doi:10.6028/nist.sp.2200-05

Certification approaches for weigh-in-motion systems in law enforcement applications

2024· report· en· W4393066833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
FundersPhysical Measurement LaboratoryNational Institute of Standards and TechnologyYork University
KeywordsCertificationLaw enforcementLawEnforcementMotion (physics)BusinessLaw and economicsComputer sciencePolitical scienceSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Every day, overweight and excessively heavy vehicles cause damage to roads, bridges, and other vehicle-based infrastructure. To protect this vital transportation infrastructure for the U.S., states have imposed weight limits for commercial and fleet transport vehicles. A common way for enforcing these weight limits is to guide trucks off the road to weigh stations where the vehicles can be weighed using static truck scales. A disadvantage of these dedicated weigh stations is that they take up a substantial amount of space (which is not always available) and time to conduct weighments, as well as cause delays to traffic flow that may impede commerce based on truck transport. A solution to these problems is the use of automatic weigh-in-motion (WIM) systems that are installed in the road and weigh vehicles as they pass by while maintaining their speed. For jurisdictions to effectively use a WIM system for direct enforcement of weight limits, the system must be evaluated against a recognized standard to establish suitability for its intended application. The vast majority of weighing instruments used for legal metrology purposes (including law enforcement) need to comply with the requirements in NIST Handbook 44. However, the NIST Handbook 44 does not (yet) cover WIM systems for direct enforcement. Although state and local jurisdictions use NIST Handbook 44 to certify legal metrological instruments, it does not exclude them from using additional technical standards to certify certain instruments. New York City recently certified a WIM system to protect a critical section of the Brooklyn-Queens Expressway (BQE) by designating it as a pilot project while efforts were made to amend NIST Handbook 44 to include WIM systems for direct enforcement. This publication discusses the main characteristics of WIM systems and how they can be used for direct enforcement. An overview of several alternative documentary standards that can be applied for certification of WIM systems is also provided, with further explanation regarding how New York City Department of Transportation (NYCDOT) implemented the certification of the WIM system to begin issuing citations to overweight vehicles in an effort to protect the BQE.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.877

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 integrity0.0010.000
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 head0.070
GPT teacher head0.256
Teacher spread0.186 · 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

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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