Modeling Pavement Performance Indices in Harsh Climate Regions
Bibliographic record
Abstract
Newfoundland and Labrador lies in the northeastern corner of North America. Road durability in this province is negatively affected by the harsh climate and ever-increasing traffic loads. The provincial five-year road plan emphasizes the maintenance and rehabilitation of existing pavements. For this purpose, basic parameters determining pavement condition should be evaluated. These include the determination of International Roughness Index (IRI), Present Serviceability Rating (PSR), and Pavement Condition Index (PCI) of roads in the city of St. John’s, Newfoundland. A smartphone application called TotalPave was used to measure IRI values. To compute PCI, ASTM International D6433-18 standard was adopted, and a questionnaire was distributed among drivers to obtain PSR. In addition, pavement distress data were collected for major and minor roads. Pavement distresses such as rutting, block cracking, fatigue cracking, longitudinal cracking, transverse cracking, delamination, potholes, and patching were analyzed, and a correlation was developed between the roughness and distress measurements. Roads were found to be in a noticeably inferior condition and PCI values correlated the most with the extent of pavement distresses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".