Correlations between curling stone frictions and tribology’s Stribeck curve: concepts to consider
Bibliographic record
Abstract
The glides of curling stones to the button on curling-sheet ice are composed of three segments: ( i) an initial high-speed travel characterized by mild decelerations (and minor curls); ( ii) a slower-speed segment with visibly greater rates of deceleration (and curl); and ( iii) an abrupt end-of-travel. It is proposed here that these three travel segments correlate with the three well-known frictional regimes of tribology’s Stribeck curve: ( i) a hydrodynamic (wet) frictional regime in which, at high speeds, the generation of frictional heat from shearing stress within a lubricating water film is sufficient to melt ice and fully isolate the rock’s running-band from the ice surface, i.e., from mutually abrasive contact; ( ii) a mixed frictional regime where, with ever-more severe friction and ever-stronger deceleration and thus ever-lower speeds, the generation of frictional heat is progressively less and the lubricating water film becomes ever-thinner, allowing rock and ice asperities to cut ever-more abrasively into their opposing surfaces; and ( iii) a totally dry, highly abrasive frictional regime in which, at very low speeds, lubrication ends because of the lack of sufficient frictional heat to maintain a water film and thus the rock’s advance comes to an abrupt end.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".