WHO RIDES AND WHO PAYS: A COMPREHENSIVE ASSESSMENT OF THE COSTS AND BENEFITS OF MOTORCYCLING IN THE UNITED STATES
Bibliographic record
Abstract
This paper offers a comprehensive assessment of the benefits and costs of motorcycle use while exploring the characteristics, behaviors and attitudes of motorcycle riders. U.S. motorcyclists are at relatively high risk of crashing, per mile travelled, with rates 24 times higher than those of passenger car and light-duty truck drivers. However, motorcycles require just one quarter the parking space of a car, and can double network capacities (in terms of vehicles per hour), thereby reducing congestion. While most motorcycles enjoy high fuel economy, their low seating capacities render them little or no better than most cars and some light-duty trucks (assuming average vehicle occupancies). They emit relatively fewer grams of CO2, NOx, SO2 and PM10 per person-mile traveled than most cars, but more VOC and CO, if a catalytic converter is not installed. Noise impacts are also a serious issue for many motorcycles, with an inconsistent patchwork of regulations applied across states and localities. Results of a survey of current and former U.S. motorcyclists indicates almost use their motorcycles for recreational purposes and ride in groups, though about half also ride for more
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".