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Record W4401758206 · doi:10.1038/s41598-024-70480-w

Analyses of the impact of laterality on performances in the National Hockey League based on players’ position and origin

2024· article· en· W4401758206 on OpenAlexafffundabout
Simon Grondin, Pier-Alexandre Rioux, Daniel Fortin‐Guichard

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoUniversité Laval
FundersSocial Sciences and Humanities Research Council
KeywordsLeagueLateralityPosition (finance)Physical medicine and rehabilitationIce hockeyComputer sciencePsychologyMedicineBusinessNeurosciencePhysics

Abstract

fetched live from OpenAlex

This study addresses the question of the lateral preference of the National Hockey League players. The shooting preference, left or right, was analysed as a function of the origin of four groups of players that are from the USA, Canada, Europe, or Russia. The analysis reveals that the players from the USA are more likely to shoot right than players from other countries. Also, compared to defense players from other groups, defense players from the USA have a higher number of shots per game and a higher goal-to-assist ratio. The study also shows that for wingers shooting left, those playing on the right wing have more goals or points per game than those playing on the left wing; and that European forward players have a better differential (+/-) than American and Canadian forward players. The study reveals the influence of the players' origin on the preference in a bimanual asymmetric task and the impact of this preference on ice hockey performances.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.116
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.085
GPT teacher head0.389
Teacher spread0.304 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes3
Has abstractyes

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