Bibliographic record
Abstract
Cyclists on rural highways travel at much lower speeds than motor vehicles.There is concern that unsteady aerodynamic loads produced as a large vehicle passes a cyclist can cause instability and loss of control, potentially initiating a traumatic accident for the cyclist.Large lateral spacing can be provided between cyclists and motor vehicles using wider paved shoulders, however this adds cost to road construction, so there is a need to balance the needs of cyclist safety and paved shoulder width.Understanding the nature of the unsteady wind loads experienced by a cyclist when a motor vehicle passes is a necessary first step in determining optimum paved shoulder widths.An experiment was conducted that directly measured the lateral forces on a full scale model cyclist, static pressure and wind speed as motor vehicles passed a cyclist.As a motor vehicle passed, the cyclist first experienced a large transient lateral forcing, followed by lower magnitude forcing.The magnitude of the force was well correlated to the measured static pressure, while induced transient wind speeds were relatively low (on the order of 1 m/s).As would be expected, the magnitude of forcing increases with vehicle size and speed, and decreases as lateral spacing between cyclist and vehicle increases.The results were used to develop an expression to predict tipping moment as a function of passing vehicle characteristics and offset distance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".