Décomposition du pseudo-profil et analyse de l'indice de rugosité international (IRI)
Bibliographic record
Abstract
In order to get the most out data from a pseudo-profile and to further develop an international roughness index (IRI) concept, a new interpretation approach of the IRI is presented. It is based on the breakdown of the initial pseudo-profile of a pavement into elementary pseudo-profiles based on various well-defined wavelengths. The underlying hypothesis to this approach is that the IRI calculated on these elementary pseudo-profiles is proportional to the contribution of each one to pavement condition. From this hypothesis, various pseudo-profile analytical techniques are presented. A few definitions and context are presented first. The following approach is then described: (i) filtering technique, (ii) calculation of the IRI for elementary pseudo-profiles, (iii) additional analytical tools. Finally, an application example describes the approach.Key words: IRI, filtering, pseudo-profile, pavement, defect, wavelength, bandwidth, profilometre, evenness.[Journal Translation]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".