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Record W4311464222 · doi:10.1038/s43246-022-00317-4

Effect of grip-enhancing agents on sliding friction between a fingertip and a baseball

2022· article· en· W4311464222 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsLeagueFriction coefficientEngineeringCoefficient of frictionForensic engineeringAdvertisingBusinessMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Friction between a pitcher’s fingers and the leather surface of a baseball is a key factor that influences ball delivery, causing Major League Baseball in the United States to recently enhance enforcement of rules banning the unauthorized use of friction-enhancing agents or sticky substances. Here, we examine how the application of rosin powder and sticky substances alters the friction coefficient between a fingertip and the leather of a baseball. We find that sticky substances increase friction which can positively affect ball spin rate, while rosin has the advantage of keeping friction consistent within and between individuals. Additionally, we find that baseballs used by the Nippon Professional Baseball Organization in Japan are less slippery compared with the ones used in Major League Baseball, suggesting that grip-enhancers may have a larger impact on friction for baseballs used in the United States compared to Japan. Furthermore, our results indicate that changing the characteristics of the leather the baseball is made from may increase friction, reducing the unauthorized use of sticky substances.

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.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.249
Teacher spread0.233 · 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