Trucking and size and weight regulations in the mid-continent corridor
Bibliographic record
Abstract
This thesis is an empirical analysis of trucking and truck size and weight (TS&W) regulations in the Mid-continent corridor. Based on this analysis, it compares and contrasts plausible near term TS&W policy options relating to this corridor. The approach of the research is to understand the corridor's TS&W regulations, trucking activity, and commodity and trade flows; with a view to facilitating the comparing and contrasting of TS&W policy options. With this understanding, the thesis then compares and contrasts the TS&W policy options. The corridor is governed by a complex set of TS&W regulations emanating directly from the U.S. Federal Government, the nine corridor States, Mexico, Manitoba, and indirectly from other jurisdictions throughout North America. These TS&W regulations have created a complex truck fleet with many different physical and operational characteristics. The total activity in the corridor is dominated by intrajurisdictional movements. However, while the corridor is often characterized as a north-south entity, much of its transportation activity in fact runs east-west to and from or through the corridor States. Also, the amount of interstate trucking that occurs within the corridor is minimal and very little north-south interjurisdictional activity takes place to and from the corridor. Much of the trucking in this corridor takes place well within the boundary conditions established by the TS&W regulations governing trucking in the corridor. Therefore, relaxation of these regulations can only be of real consequence in the near to medium term to mainly selected aspects of the total trucking activity. (Abstract shortened by UMI.)
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".