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Record W7028985268

On-ice acceleration as a function of the Wingate anaerobic test and a biomechanical assessment of skating technique in elite ice hockey players

2017· dissertation· en· W7028985268 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldMedicine
TopicDermatological diseases and infestations
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsAccelerationAnaerobic exerciseSprintPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

Success in ice hockey depends on an individual?s ability to accelerate from a standing \nstart or change direction and continue skating quickly and efficiently. Previous research to \ndetermine those factors which had the greatest contribution to on-ice acceleration was limited to \ntwo-dimensional biomechanical analyses of skating technique, without regard for the influence \nof physiological measures. The purpose of the present study was therefore to predict on-ice \nacceleration using peak anaerobic power from a Wingate test and kinematic variables from a \nthree dimensional analysis of the biomechanics of skating technique. A sub-purpose of the \npresent study was to examine the variability of skating technique at the elite level. The \nparticipants in this research study were thirty-seven ice hockey players from the Florida Panthers \nand Los Angeles Kings of the National Hockey League participating in the 1999 Prospects Camp \nin Thunder Bay, Ontario. The players completed a thirty second, maximal intensity Wingate \nanaerobic cycle ergometer test against a resistance of 0.095 kg-kg bodyweight-1. Peak anaerobic \npower was calculated and recorded as the highest anaerobic power value (number of flywheel \nrevolutions) produced during any of the five-second intervals. One week following the Wingate \nanaerobic test, the players performed two maximal, on-ice accelerations over a distance of \ntwenty meters, while being taped by two, Panasonic? CL-350 digital cameras mounted on Peak \nPerformance? pan/tilt heads. The Peak Performance? 3D Video Analysis System and a 23- \npoint spatial model were used to extract the raw coordinates for the fastest of the two trials for \neach player, as measured by a photoelectric timer. The system was then used to smooth the raw \ndata from both camera views and to combine the smoothed data to produce a three-dimensional \nimage. Center of mass and kinematic variables of interest were measured at push-off and \ntouchdown for the first five strides.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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