Modelling the performance of pavement marking in cold weather conditions
Bibliographic record
Abstract
Inadequate and poorly maintained pavement markings are considered to be one of the largest contributing factors to fatal motor vehicle crashes. As a result, it is essential to apply the appropriate pavement marking material for all weather conditions in order to increase public safety and reduce motor vehicle crashes. Building a strategic plan to renew and re-stripe pavement marking is receiving increasing interest from companies/authorities that manage the pavement marking in order to reach the most cost-efficient management plan of the available pavement marking materials. The objective of this paper is to develop pavement marking performance models that predict the condition of different marking materials under various service conditions including weather, traffic and snow removal plans. The developed models are validated and the results show that the average percent validity varies from 87% to 99%. Marking performance is assessed using a condition rating scale, which numerically ranges from 1 to 5 and linguistically from excellent to critical, respectively. Deterioration curves are developed that assess the condition of the pavement marking based on the developed models. They are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritise inspection, stripping, and re-stripping planning for various pavement markings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".