El Gendy and Shalaby 1 DETECTING LOCALIZED ROUGHNESS USING DYNAMIC SEGMENTATION Word Count (including tables and figures): 5,443
Bibliographic record
Abstract
Monitoring pavement surface characteristics is an essential element of pavement management systems. The objective of this paper is to develop guidelines for analyzing the measurement of longitudinal pavement profile using two dynamic segmentation methods and to demonstrate the benefits of refining the monitoring of pavement conditions. In the Canadian Long-Term Pavement Performance database, C-LTPP, a representative average value of the international roughness index (IRI) is stored for each of the longitudinal profiles. However, for maintenance work, it is useful to store the changes in roughness at a higher level of detail. The roughness profiles for all C-LTPP sections are calculated based on a constant base length. The roughness profiles have been analyzed to evaluate the effect of localization of roughness values. The IRI range, which is the difference between the maximum and minimum IRI is calculated for each of the 1332 roughness profiles. It is found that most of the monitored profiles have an IRI range that exceeds 1.0 m/km. These high ranges mean that the average IRI for a section is not sufficient to give detailed information about how rough the section is. For
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".