Bibliographic record
Abstract
A modified water spray endurance test (WSET) was adapted from SAE Standard AS5901B and used to study the anti-icing endurance of a variety of sodium chloride–based highway anti-icer liquids. Freezing precipitation immediately forms rime ice on surfaces with no anti-icer treatment. On surfaces treated with anti-icer, freezing precipitation goes through a series of stages and initially forms a liquid brine layer, then nonadherent glaze ice, and finally rime ice. The modified WSET was used to study the effect of a variety of additives in sodium chloride anti-icing liquids. High-molecular-weight organic polymer additives were found to strongly inhibit the onset of rime ice formation. The delay in the onset of rime ice formation caused by anti-icers is postulated to be the result of an enhancement of surface melting, increasing the size of the quasiliquid layer at the ice surface and favoring glaze rather than rime ice formation until the anti-icer has been sufficiently diluted. The same surface melting enhancement caused by anti-icer chemicals may also contribute to the observed effectiveness of low concentrations of anti-icer in preventing ice adhesion to pavement surfaces. In this context, the use of the modified WSET to evaluate rime ice growth rates is discussed as a possible way to compare the relative precipitation resistance (and therefore “effective lifetime”) of different anti-icer formulas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".