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Record W2068738642 · doi:10.1519/r-20636.1

Comparison of a Kayaking Ergometer Protocol With an Arm Crank Protocol for Evaluating Peak Oxygen Consumption

2007· article· en· W2068738642 on OpenAlexaff
Scott C. Forbes, Philip D. Chilibeck

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCrankCycle ergometerBicycle ergometerMedicineProtocol (science)Significant differencePhysical therapySimulationHeart rateMathematicsInternal medicineBlood pressureComputer scienceGeometry

Abstract

fetched live from OpenAlex

The purpose of this study was to compare a kayak ergometer protocol with an arm crank protocol for determining peak oxygen consumption (V(.-)O2). On separate days in random order, 10 men and 5 women (16-24 years old) with kayaking experience completed the kayak ergometer protocol and a standardized arm crank protocol. The kayak protocol began at 70 strokes per minute and increased by 10 strokes per minute every 2 minutes until volitional fatigue. The arm crank protocol consisted of a crank rate of 70 revolutions per minute, initial loading of 35 W and subsequent increases of 35 W every 2 minutes until volitional fatigue. The results showed a significant difference (p < 0.01) between the kayak ergometer and the arm crank protocols for relative peak V(.-)O2 (47.5 +/- 3.9 ml x kg(-1) x min(-1) vs. 44.2 +/- 6.2 ml x kg(-1) x min(-1)) and absolute peak V(.-)O2 (3.38 L x min(-1) +/- 0.53 vs. 3.14 +/- 0.64 L x min(-1)). The correlation between kayak and arm crank protocol was 0.79 and 0.90, for relative and absolute V(.-)O2 peak, respectively (both p < 0.01). The higher peak V(.-)O2 on the kayak ergometer may be due to the greater muscle mass involved compared to the arm crank ergometer. The kayak ergometer protocol may therefore be more specific to the sport of kayaking than an arm crank protocol.

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)0.006
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 head0.166
GPT teacher head0.512
Teacher spread0.346 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

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