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Record W2744859900 · doi:10.1177/1747954117718075

Energy expenditure by elite midget male ice hockey players in small-sided games

2017· article· en· W2744859900 on OpenAlexaff
Charles-Mathieu Lachaume, François Trudeau, Jean Lemoyne

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2017
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIce hockeyHeart rateEliteEnergy expenditurePsychologyTreadmillPhysical therapyPhysical medicine and rehabilitationMedicinePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the energy expenditure and heart rate responses elicited in elite male midget ice hockey players during small-sided games. Nine players (aged 15.89 ± 0.33 years) participated in the study. Maximal progressive treadmill testing in the laboratory measured the relationship of oxygen consumption ([Formula: see text]) to heart rate before on-ice assessments of heart rate during six different small-sided games: 1v1, 2v2, 2v2 with support player, 3v3 with support player, 3v3 with transitions, and 4v4 with two support players. Heart rate was recorded continuously in each game. 3v3 T small-sided game was the most intense for all four intensity markers. All six small-sided games reached 89% HR max or more with heart rate peaks in active effort repetition. These findings demonstrate that such small-sided games are considered as high intensity games and are an effective training method for ice hockey players.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.019
GPT teacher head0.306
Teacher spread0.287 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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