Bibliographic record
Abstract
Although the study of friction has a long history, ice friction has only been investigated during the last century. The basic physical concepts underlying the different friction regimes, such as boundary, mixed, and hydrodynamic friction are also relevant to ice friction. However, these friction regimes must be described with respect to the thickness of the lubricating liquidlike layer on ice. In this review the state of knowledge on the physics of ice friction is discussed. Surface melting theories are introduced. These theories attempt to explain the existence and nature of the liquidlike surface layer on ice at any temperature and without any load applied. Pressure melting, as the long-time explanation for the ease of ice friction, is discussed, together with the prevailing theory of frictional heating. The various laboratory setups for ice friction measurements are presented as well as their advantages and disadvantages. The individual influence of the different parameters on the coefficient of ice friction is discussed; these include the effects of temperature, sliding velocity, normal force exerted by the sliding object, the contact area between ice and slider, relative humidity, and also properties of the slider material such as surface roughness, surface structure, wettability, and thermal conductivity. Finally, the most important ice friction models based on the frictional heating theory are briefly introduced and research directions on the subject of ice friction are discussed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".