Options for Hauling Fully Loaded ISO Containers in the United States
Bibliographic record
Abstract
Civil engineers plan, design, manage, and guide investments in surface transportation. Truck weight limits are inextricably linked to the consideration of pavements, bridges, safety, asset management, sustainability, jobs, and economic productivity. The U.S. 36.3 t cap for gross vehicle weight (GVW), and the Federal Bridge Formula B (FBF B) constrain truck movement of 32.5 t full ISO (International Organization for Standardization) containers. This study identifies the options—and associated implications—for trucks to transport full containers notwithstanding FBF B and the cap. We conducted a national investigation of truck weight regulations across 50 states to identify options, with 28 cases involving telephone interviews. Results show the options are complex, vary by state, result in a patchwork of routes, and include: (1) nondivisible permits, (2) special haul routes, (3) grandfather rights, (4) state roads, (5) tare reduction, and (6) operation outside formal regulations. Containerships, railroads, ports, and highways of U.S. trading partners each accommodate full containers. Conditions identified in this paper can result in underutilization of the productivity and efficiency benefits afforded by international standardization.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".