Bibliographic record
Abstract
This paper presents a comprehensive, multidimensional evaluation of the federal interstate commercial driving hours-of-service (HOS) rules implemented on January 4, 2004. The study analyzes changes in fleet crash rates following the rule change and reactions of industry personnel (drivers and fleet safety managers) to the new rules. Information on industry personnel reaction to the new HOS rules was assessed through focus groups and a survey of nearly 1,000 drivers. In general, industry personnel like the new rules. Pluralities of drivers say that driving is easier and they are less fatigued. Drivers and companies like the 34-h restart but do not like the 14-h daily restriction on tour-of-duty duration. The study also gathered and aggregated 2003 and 2004 data from 23 mostly medium to large trucking fleets on vehicle mileage, DOT-reportable collisions, and driver injuries. Altogether, these 23 fleets traveled more than 10 billion miles annually in 2004 and represented approximately 8% of all large-truck mileage in the United States. This aggregated, “within subjects” comparison of HOS-related fleet safety and health data is the largest such compilation ever performed in the United States. Aggregated statistics for the 23 fleets showed statistically significant decreases in rates of collision (–3.7%) and preventable collision (–4.7%). There were even greater reductions in rates of driver injury (–12.6%) and driver collision-related injury (–7.6%). Taken together, these findings document that the trucking industry reaction has generally been favorable to the new HOS rules and that implementation of the new rules has been accompanied by a reduction in collision and injury rates.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".