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Record W3034434260 · doi:10.3390/proceedings2020049082

Comparing Broom Conditions in Curling: Measurements Using Ice Topography

2020· article· en· W3034434260 on OpenAlexaff
Megan E. R. Balsdon, J.T. Wood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsCurlingCurl (programming language)BroomGeologySurface roughnessSurface finishHourglassOpticsMineralogyMaterials sciencePhysicsComposite materialAstronomyGeography

Abstract

fetched live from OpenAlex

The sport of curling is played on an ice surface with raised ice pebbles and uses curling rocks made of granite. The effect of sweeping is thought to straighten the rock’s projected curved path and increase the distance travelled. Recent anecdotes suggest that sweeping from the center of the running surface with the direction of rotation and curl is thought to increase the amount of curl, whereas sweeping against and opposite the curl is thought to decrease the amount of curl. The purpose of this study is to observe the topography of the ice surface while comparing scratch measurements from different broom materials. Nine conditions were replicated: nipped pebble, rock traversing the ice, and seven broom conditions. Replicas of the ice were created with vinyl polysiloxane and observed with an optical microscope. Roughness profiles of the replicas were measured, and broom materials were compared using data from an optical profiler.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.406

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 head0.176
GPT teacher head0.344
Teacher spread0.168 · 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 designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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