An overview of various new road profile quality evaluation criteria: part 2
Bibliographic record
Abstract
This is the second part of an article which correlates road-induced impacts on vehicle to a selection of road assessment criteria. The impacts on tyre, suspension and radiator wear are studied by running a multibody semi-trailer truck model on 270 road profiles. The model accurateness is assessed by comparing international roughness index (IRI)–impact relationships to those published in the literature. A new profile rating method uses wavelength content to predict the impacts of a specific profile on driver and passenger health and safety, truck wear and fuel consumption. It is concluded that (1) medium wavelengths severely impact fuel consumption, component wear and safety; (2) simple, two-point and four-point indices yield similar results, but the more the points the better the correlation; (3) the IRI is good at predicting general trends in road-induced vehicular impact but is weak for specific impacts and (4) tyre wear correlates linearly while component wear requires quadratic correlations.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".