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Record W2511247687 · doi:10.1080/17461391.2016.1220627

Effect of the look‐up line on the gaze and head orientation of elite ice hockey players

2016· article· en· W2511247687 on OpenAlexaff
Joan N. Vickers, Joe Causer, Michael Stuart, Elaine C Little, Sean P. Dukelow, Marc Lavangie, Sandro Nigg, Gina Arsenault, Barry Morton, Matt Scott, Carolyn A. Emery

Bibliographic record

VenueEuropean Journal of Sport Science · 2016
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsSkateGazeThrowingSimulationGeodesyComputer scienceMathematicsArtificial intelligenceGeographyEngineeringAeronautics

Abstract

fetched live from OpenAlex

A "look-up line" (LUL) has been proposed for ice hockey, which is an orange 1 m (40') warning line (WL) painted on the ice at the base of the boards. The LUL purports to provide an early warning to players to keep their head up prior to and as they are being checked. We determined if players looked up more on a rink with the LUL compared to a traditional Control rink. Elite offensive (O) and defensive (D) players competed 1 vs. 1, while wearing an eye tracker that recorded their quiet eye (QE) and fixation and tracking (F-T) and an electrogoniometer that measured head angle. External cameras recorded skate duration during four skate phases: P1 preparation, P2 decision-making, P3 cut to boards, P4 contact. The QE was the final fixation prior to contact between O and D as they skated towards and across the WL during P3 and P4. Skate phase durations (%) did not differ by rink or rink by position. More QE and F-T occurred on the WL on the LUL rink than on the Control. The expected increase in head angle on the LUL rink did not occur during P3 or P4. Post-hoc results also showed O and D skated further from the boards on the LUL rink, suggesting the players preferred to control the puck on white ice, rather than the orange colour of the LUL rink. More research is needed to determine if these results apply to the competitive setting.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.022
GPT teacher head0.343
Teacher spread0.321 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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