Bibliographic record
Abstract
Growing travel and trade between the United States and its neighbors, Canada and Mexico, make border crossings a key contributor to the Nation's economic health. This article describes the work of the Federal Highway Administration (FHWA) in a number of initiatives with its State, Federal, and international partners to address the challenges of improving mobility and security at overland border crossings. With its counterparts in Mexico and Canada, FHWA created joint working groups that cooperate on planning and facilitating cross-border movements. In addition, FHWA is involved in initiatives with other agencies and organizations to share technologies, streamline the movement of cargo trucks across borders, adopt innovative tools to plan border-crossing improvements, create frameworks that enable key technologies to work together, and measure success in achieving objectives in global connectivity. The author describes the work in these areas, illustrated with examples from border crossings in California, Michigan, Texas, Idaho, New York, and Arizona. The author then discusses a new software tool, Border Wizard, that simulates cross-border movements. The software can create a model showing a specific port of entry and can summarize the movement of automobiles, buses, trucks and pedestrians, the number of booths, Federal inspection activities, and other information such as wait times in hours. The author concludes by considering the role of continuing partnerships in achieving global connectivity and thus, improved productivity.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".