Bibliographic record
Abstract
This paper provides a review of the practice the States use in permitting super heavy commercial vehicles (SHCV) or “superloads”. The data presented were obtained through a literature review and a survey questionnaire circulated to State DOTs. The literature review revealed significant differences in defining SHCVs and assigning permit fees. GVW limits vary from 534 to 1,130 kN, while axle loads limits vary from 89 to 129 kN for single axles and from 151 to 267 kN for tandem axles. SHCV permit fees and the methodology used to assign them also vary significantly between agencies: 23 agencies (37%) levy fees on the basis of weight-distance (i.e., fees range from $0.003/tonne/km to $0.11/tonne/km), 15 agencies (24%) levy fees that depend on GVW/axle weight only and 8 agencies (13%) levy a flat fee. A total of 39 States and 5 Canadian Provinces responded to the survey questionnaire (i.e., 8 States submitted two responses, one by their permit officer and one by the engineer that analyzed the impact of SHCVs, bringing the total number of survey responses to 52). 38 agencies responded as to whether or not they conduct pavement analysis as part of their SHCV permit process. Of those, 5 (13%) always do, 15 (40%) do so depending on the circumstances, while the remaining 18 agencies (47%) never perform such an analysis. Details on the pavement analysis performed were provided by 15 agencies. Their majority uses either their own in-house developed mechanistic-empirical pavement analysis approach or the mechanistic methods developed by industry (e.g., Asphalt Institute or Portland Cement Association). Several agencies indicated that they use the 1993 AASHTO Pavement Design method. None of the responding agencies uses the Mechanistic-Empirical Pavement Design Guide for analyzing the impact of SHCV.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".