Assessing Impact of Automated Commercial Environment Truck e-Manifest
Bibliographic record
Abstract
Sources estimate that 25 percent of the U.S. GDP is associated with international trade, a figure that is expected to grow in the near future. Due to this likely increase in trade, the number of trucks entering the U.S. from Canada and Mexico is expected to increase considerably. In light of this, the federal government is tasked with managing the competing objectives of maximizing economic vitality and ensuring the security and integrity of U.S. borders. In order to reach these goals, U.S. Customs and Border Protection (CBP) developed the ACE Truck e-Manifest which intends to manage rival goals by allowing motor carriers to submit electronic versions of mandated paperwork in advance of the truck physically crossing the border. American Transportation Research Institute (ATRI) recently worked with CBP to conduct an analytical assessment of the productivity and efficiency impacts of the ACE Truck e-Manifest through surveys, site visits and interviews to determine impacts on motor carrier operations. The results show a more efficient border crossing process for carriers using e-Manifests. However, workload and costs for those carriers have increased. Although initial start-up costs are considerable for many carriers, e-Manifests can potentially provide net operational benefits for medium and large carriers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".