4. Title and Subtitle Characterizing Truck Traffic in the U.S.-Mexico Highway Trade Corridor and the Load Associated Pavement Damage 6. Performing Organization Code 7. Author(s)
Bibliographic record
Abstract
Supported by general revenues from the State of Texas. As the biggest asset in the transportation infrastructure system, highways play a critical role in a nation’s economic development. Paradoxically, while this development serves as a driving force it is also responsible for significant damage to the highway infrastructure. The United States, together with Mexico and Canada, signed the North American Free Trade Agreement (NAFTA) in 1992 in an effort to eliminate a large number of tariff barriers to free trade and thus to enhance the economic development of the three countries. Since the ratification of NAFTA in November 1993, U.S. trade with Canada and Mexico has increased dramatically, resulting in a significant increase in truck movements in the countries. In 2004, the Supreme Court ruled against the requirement to undertake an environmental impact study before opening the U.S.-Mexico border, which paved the way for U.S. roads to be opened to long-haul Mexican carriers under NAFTA. As a result of truck traffic surge, from the perspective of infrastructure preservation, concerns have been raised by highway agencies in the bordering states regarding the increased damage by the growing traffic. Because of Texas ’ proximity to the industrial heartlands of both Mexico and the U.S., the Texas transportation
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.056 |
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".