Dynamic segmentation of smartphone sensor-based pavement functional condition
Bibliographic record
Abstract
Roads form a vital part of the infrastructure as it influences peoples’ lives directly in terms of connectivity as well as mobility. By using traditional methods for evaluating the pavement condition, the authorities spend a significant amount of resources. Automated techniques such as image processing and laser imaging systems can be adopted to overcome this issue. However, it needs complicated additions such as special lights and lasers that exponentially increases the surveying cost. In this explorative research, an effort was made to study the applicability and reliability of smartphone sensors in pavement functional condition monitoring. For efficient management of pavement for maintenance and rehabilitation activities, identifying the representative sections of these pavements would be more effective. A dynamic segmentation method is adopted to segment the smartphone-based pavement functional condition. The pavement was classified into five groups with 0–2, 2–4, 4–6, 6–10, and >10 m/km IRI values, as in Roughometer III.
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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 | 0.000 | 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".