Modelling the performance of pavement marking in cold weather conditions
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
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.
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 | 0.001 |
| 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