Roll dynamics of long combination semi-trailers with steerable axles
Bibliographic record
Abstract
An assessment of the dynamic performance of long combination vehicles (LCV) with steerable axles was undertaken to facilitate the regulation of LCVs for wider use on Canadian roads. A base dry box van A-train LCV and four steered combinations with different steerable axle mechanisms on the trailer are modelled using TruckSim. TruckSim's driver logic is augmented through an optimised controller to more accurately capture the driver's decision-making in response to the LCV dynamics. Anti-lock braking system (ABS) and traction control mechanisms are added to compensate for the reduced stability caused by improving the manoeuvrability. The models are run through highspeed lane change and turn simulations, the primary manoeuvres for the assessment of roll dynamics of LCVs. The configurations are compared in terms of standard performance parameters: static roll threshold, rearward amplification (RWA), load transfer ratio (LTR), highspeed off-tracking and transient off-tracking. All mechanisms are shown to satisfy standard stability requirements.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".