Dynamic rollover threshold of articulated freight vehicles
Bibliographic record
Abstract
Different measures of relative roll instability of heavy vehicles are investigated to determine their dynamic rollover characteristics. Analytical models of different vehicle combinations are presented and a concept of effective lateral acceleration is proposed to characterize the relative roll instability under dynamic directional manoeuvres. The analytical models for a five–axle tractor semi–trailer combination and an eight–axle A–train double are analysed to establish the dynamic rollover threshold based upon relative roll instability criterion and effective lateral acceleration. The dynamic rollover threshold of the vehicle, derived for different suspension properties and operating conditions, is compared with the corresponding static rollover threshold of the vehicle. From the results of the study, it is established that dynamic rollover threshold based on effective lateral acceleration in most cases is either slightly lower or equal to the static rollover threshold acceleration. The difference between the dynamic and static rollover thresholds is less than 5% for the vehicle configurations and the steering manoeuvres considered in the study. The static rollover threshold may thus be conveniently employed to estimate the dynamic rollover propensity of heavy vehicles.
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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 | 0.001 | 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".