The ice melting efficacy and environmental impact of alternative de-icers
Bibliographic record
Abstract
The application of sodium chloride (NaCl) to maintain safe, passable transportation and infrastructure in Canada can adversely impact the environment, including salinization of freshwater resources and mobilization of heavy metals from soil, thereby motivating efforts to assess alternative salts. This thesis aims to provide a holistic overview of conventional and innovative de-icers, namely calcium chloride (CaCl2), magnesium chloride (MgCl2), potassium chloride (KCl), sodium acetate (NaOAc), calcium magnesium acetate (CMA), chloride free brine (CFB), and sugar brine (SB), for their ice melting efficacy and environmental impact. The ice-melting and ice-penetration ability of these alternative de-icers were examined in the laboratory following standardized procedures from the Strategic Highway Research Program (SHRP) test methods and in the field at a non-trafficked parking lot. A comprehensive suite of batch tests was performed to investigate the mobilization of 10 metals from a common roadside soil when exposed to various concentrations of each alternative de-icer. Results from this thesis showed that all de-icers experienced at least a 49% reduction in their ice-melting capacity when the temperature decreased from -6.7°C to -17.7°C. MgCl2 exhibited the highest ice-melting ability of all de-icers and was the only effective de-icer below -12.2°C. In terms of metal mobilization, the de-icers releasing the highest number of different metals (in decreasing order) were: (1) CaCl2 and SB, (2) KCl and MgCl2, (3) CMA, (4) NaCl, (5) CFB, and (6) NaOAc. The findings from this study can provide policy makers, municipalities, and other road salt users with more knowledge on the performance of alternative de-icers to allow them to make informed decisions on road salt application in Canada.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".