Bio-based materials for improving winter pavement friction
Bibliographic record
Abstract
Over five million tons of salt (NaCl) is applied in Canada every winter to improve pavement friction in the winter season. While effective for improving pavement surface condition, salts at high concentrations are detrimental to the environment and corrosive to vehicles and infrastructure. New alternative salts (bio-based products), are increasingly available in the market as an alternative to regular salt; however, limited information on the performance of these alternatives is available for transportation agencies to make informed decisions for their usage. In this study, a set of bio-based products were selected and their performances were compared using pavement friction improvement as a measure. A multivariate linear regression analysis was conducted to identify the factors influencing pavement friction by utilizing these new materials. The analysis has indicated that using bio-based materials resulted in 10%–40% improvement in the friction level. However, these materials did not significantly outperform each other. The study also concluded that an application rate as low as 3 L/1000 ft 2 should be applied for parking lots or low volume roads, which is 25% less than the current application rates that are used in general for parking lot pavement maintenance.
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.
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 | 0.000 | 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) | 0.002 | 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".