The verification and demonstration of a unique course-profile-measurement methodology
Bibliographic record
Abstract
<title>ABSTRACT</title> <p>This paper presents an experimental methodology for regeneration of course profiles, with tire acceleration and speed, suspension force and pitch data collected from an instrumented trailer wheel running over the course profile to be identified. The collected data is used to derive the course profile as a function of time, while the speed data is used to map the ground elevation data from a time domain into a spatial domain. To verify the course re-generating rationale, the required test data was simulated with the data extracted from a trailer model running over selected course profiles. The regenerated course profiles are then compared against those used as the inputs to the trailer model, demonstrating the feasibility of using the methodology to regenerate course profiles which statistically align with real-world course profiles. The methodology may be used to develop the inputs statistically equivalent to realistic course profiles as needed in dynamic simulation of terrain-vehicle systems.</p>
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".