TRB SPECIAL REPORT: REGULATION OF WEIGHTS, LENGTHS, AND WIDTHS OF COMMERCIAL MOTOR VEHICLES
Bibliographic record
Abstract
Federal and state regulations govern the weight and dimensions of trucks, buses, and trailers on U.S. highways. The regulations have economic consequences--trucking accounts for four-fifths of expenditures on freight transportation in the United States, and trucking costs are influenced by truck size and weight. Size and weight limits also influence highway construction and maintenance costs and the convenience and safety of highway travel. In addition, the regulations affect international commerce, because Canada and Mexico have different limits, and because international containers often do not meet U.S. standards. In June 1998, in the Transportation Equity Act for the 21st Century, Congress directed the Secretary of Transportation to request the Transportation Research Board (TRB) to conduct a study of the regulation of weights, lengths, and widths of commercial motor vehicles operating on federal-aid highways under federal regulation, and to develop recommendations. This article reviews TRB Special Report 267, which contains the results of the TRB study. The study recommends organizational arrangements to promote reform of federal regulations for commercial motor vehicles, as well as changes to improve the efficiency of freight transportation and to reduce the public costs of truck traffic.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".