Field Evaluation of Different Pre-Wetting Ratios for Sustainable Salting
Bibliographic record
Abstract
This research presents the findings from a field study aimed at comparing the performance of different pre-wet (PW) ratios of salt for their impacts on snow melting performance/friction of road surfaces. The research was motivated by the question of whether or not better snow melting performance can be achieved by using higher PW ratios. Field tests were conducted on three sections of a provincial highway, located in Western Ontario in the winter season of 2016/2017 under three PW ratios, 5% (current practice), 10%, and 20%. Promelt Mag 22 (22% magnesium chloride, MgCl 2 , concentration) was used for pre-wetting. Based on a comprehensive statistical analysis of the field testing data, it was found that salt pre-wetted at 20% improved friction levels by approximately 12% while reducing the salt usage by 19% and sand by 35% when compared with usages at a PW ratio of 5%. Examination of images collected during snow storms showed that sections treated with salts with higher PW ratios generally had lower snow coverage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".