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Record W2135285506 · doi:10.1139/h08-107

Sport-specific assessment of lactate threshold and aerobic capacity throughout a collegiate hockey season

2008· article· en· W2135285506 on OpenAlexvenueno aff
John J. Durocher, Darin Leetun, Jason R. Carter

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2008
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersMichigan Technological University
KeywordsBlood lactateAnimal scienceAerobic capacityAerobic exerciseLactate thresholdHeart rateMedicineAnaerobic exercisePhysical therapyInternal medicineBiologyBlood pressure

Abstract

fetched live from OpenAlex

The purpose of this study was to examine lactate threshold (LT) and maximal aerobic capacity with a sport-specific skating protocol throughout a competitive season in collegiate hockey players. We hypothesized that maximal aerobic capacity and skating velocity at LT would increase as the season progressed. Sixteen Division I college hockey players performed a graded exercise skating protocol to fatigue at 3 different times (pre-, mid-, and postseason). Subjects skated for 80 s during each stage, followed by 40 s of rest to allow for blood lactate sampling. Velocity at LT was similar during preseason (4.44 +/- 0.08 m.s-1) and postseason (4.52 +/- 0.05 m.s-1) testing, but was significantly elevated at midseason (4.70 +/- 0.08 m.s-1; p < 0.01), compared with preseason. In contrast, LT as a percentage of maximal heart rate (HRmax) was unchanged throughout the season. HRmax remained constant throughout the season, at approximately 190 beats.min-1. Preseason maximal aerobic capacity (48.7 +/- 0.8 mL.kg-1.min-1) was significantly higher than that at postseason (45.0 +/- 1.1 mL.kg-1.min-1; p < 0.01). In conclusion, skating velocity at LT improved from pre- to midseason, but this adaptation was not maintained at postseason. Additionally, maximal aerobic capacity was reduced from pre- to postseason. These findings suggest a need for aerobic training throughout the college hockey season.

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)0.000
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 score0.885
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.280
Teacher spread0.247 · 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

Citations27
Published2008
Admission routes1
Has abstractyes

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