ITS in Control on the Border. Advanced ITS Truck Screening Aids Border Control
Bibliographic record
Abstract
This article describes how state-of-the-art intelligent transportation system (ITS) technologies are being deployed for tracking of commercial vehicles at the United States (US)-Mexico border in Arizona. The border between the US and Mexico may be the epitome of America's wild west, but this remote desert frontier is being tamed by the Arizona Department of Transportation (ADOT) with a state-of-the-art ITS system. A comprehensive port-of-entry (POE) screening system is being deployed at the Mariposa Port of Entry, which is one of the busiest land ports in the nation. This particular border crossing near Nogales, Arizona has the important role of tracking commercial vehicles. The Mariposa Port of Entry serves as the main entry point for fresh produce entering the US from Mexico. It is also a key link in the CANAMEX Trade Corridor, a freight transportation route linking Mexico, the US and Canada which is considered a high priority corridor by the US government. The POE system will pre-screen vehicles that pass over each of the seven lanes at the border crossing. This new cutting-edge solution serves as an excellent example of the essential role ITS can play in transportation management at border crossings. When deployed at international border crossings, ITS technologies benefit commercial vehicle operators and carriers as well as the enforcement agencies by allowing compliant vehicles to be identified in real time so they can cross the borders with minimal delay. ITS technologies in combination with electronic screening and agency specific business rules enable enforcement agencies to specifically tailor their strategies for non-compliant vehicles, resulting in the most effective use of resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".