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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".