Field Evaluation of Organic Materials for Winter Snow and Ice Control
Bibliographic record
Abstract
Over five million tons of road salts are applied in Canada every winter to keep users of roads, parking lots and sidewalks safe. While effective for snow and ice control, salts at high concentrations are detrimental to the environment and corrosive to vehicles and infrastructure. New alternative 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 and maintenance industry to make informed decisions. In this study, a set of organic and semi-organic based products were selected and their performances were evaluated through a series of field tests. Approximately 600 tests were conducted in a real world environment for over 35 test days. The performances of the alternatives were compared using friction improvement as a measure. A multiple linear regression analysis was conducted to identify the factors influencing the performances of the alternatives. The analysis has indicated that anti-icing treatments resulted in 10%-40% improvement in the friction level. However, the alternative products did not significantly outperform each other. The study also concluded that an application rate as low as 3L/1000sqft 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 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".