Towards a First Principles Model of Curling Ice Friction and Curling Stone Dynamics
Bibliographic record
Abstract
Scientific investigations to measure and explain the curl (lateral displacement) of a granite stone sliding on ice in the sport of curling go back almost 100 years (Harrington, 1924). Nevertheless, some prominent researchers in the field remain baffled as to the physical explanation of this lateral displacement. And no one has thus far been able to produce a quantitative model, from first principles, that predicts all the documented characteristics of the observed curl. In this paper, we describe our progress towards producing such a numerical model. After reviewing the history of scientific research on curling, we summarize the experimental observations (quantitative and qualitative) that need to be explained by any successful model; we also summarize the areas of general agreement. Lastly, we formulate a numerical model of ice friction for a non-rotating curling stone, which successfully predicts its longitudinal deceleration. The model also successfully predicts the rotational deceleration of a non-translating curling stone. However, a numerical model for the lateral acceleration that produces the observed curl remains elusive. Copyright © 2015 by the International Society of Offshore and Polar Engineers (ISOPE).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".