Evaluating Performance of Alternative Deicers for Winter Road Maintenance: Deicing and Corrosivity
Bibliographic record
Abstract
Investigation on efficiency and environmental impacts of different deicers. Four experiments were conducted. For the first three experiments, there were seven alternative deicers and/or rock salt used for every scenario. The first two tests were an ice melting capacity and ice penetration test. The experiments determined each deicer's melting and penetration rate at three different loading rates and temperatures. The results show that all alternative deciers were comparable to road salt in every aspect. Next was a corrosivity test, the deicers were dissolved in water to create a brine solution where rebar pieces were soaked. The test did a soak/dry cycle for four weeks with results showing all alternative deicers outperforming rock salt in amount of rust created. The final test was the bare pavement regain efficiency of rock salt in a field setting. The field results were not promising from the ice-melting perspective for winter road maintenance.
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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) | 0.000 | 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 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".