Certification approaches for weigh-in-motion systems in law enforcement applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".