Investigation of Illegal Weigh Station Bypassing
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".