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Record W4402314306 · doi:10.1139/cjp-2024-0095

Correlations between curling stone frictions and tribology’s Stribeck curve: concepts to consider

2024· article· en· W4402314306 on OpenAlexvenueno aff
A. C. Brown

Bibliographic record

VenueCanadian Journal of Physics · 2024
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCurlingPhysicsTribologyTheoretical physicsClassical mechanicsStatistical physicsMechanical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.330
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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