Factors Influencing the Formation, Adhesion, and Friction of Ice
Bibliographic record
Abstract
Humans have faced the challenges and opportunities afforded by ice accumulation throughout our collective history. From the icing over of hunting plains to the accretion of ice on aeroplanes, the challenge of frozen water has shaped us as a species. In many ways, overcoming the challenge of surface ice accumulation is inextricably linked to human modernity. We have reached a point in engineering history where some of the most important unanswered questions cannot be fully resolved without the management and prevention of surface ice. These engineering challenges include: the complete implementation of renewable energy sources such as photovoltaic panels, wind turbines, and the requisite electrical transmission lines, the ushering in of the age of environmentally-friendly air travel, including the elimination of de-icing fluids, and the introduction of fully autonomous vehicles which will require sensors that are perpetually free of surface ice and roadways that are reliably ice free. This chapter begins with a brief history of ice on Earth, followed by an overview of how humans have faced ice accumulation in the past and how advances in technology during the first two Industrial Revolutions have facilitated our understanding of ice formation. Next, we discuss the ice formation process in terms of embryo nucleation. This is followed by a discussion of the factors influencing ice adhesion, specifically the important relationship between surface morphology and ice adhesion strength. Finally, the origins of ice's low friction is discussed in the last section.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".