On dynamic stability evaluation methods for long combination vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The dynamic stability of long combination vehicles (LCVs) is an important part of vehicle safety. An LCV, its driver and the road constitute a unique closed-loop dynamic system. Assessing the dynamic stability is difficult due to the complex interactions of driver-tractor-trailers-road. Rearward amplification (RA) is an effective performance measure of the dynamic stability; various methods are applied for evaluating the RA. However, the measures from different methods may differ significantly. What are the root causes for the disparity of evaluation results? This paper tackles the problem by investigating the typical methods for evaluating the measure of two LCVs, i.e. an A-train double and a B-train double. To this end, simulations of the LCVs are conducted using TruckSim software. The study discloses the main causes for the disparity of the measures from different methods, and recommends the effective approaches to the assessment of the RA.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it