Modeling Pavement Performance Indices in Harsh Climate Regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it