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Record W2219502499

A BIOMECHANICAL AND PHYSIOLOGICAL COMPARISON OF OLYMPIC FLATWATER CANOEING

2008· article· en· W2219502499 on OpenAlexaboutno aff
L.E. Holt, Philip D. Campagna

Bibliographic record

VenueISBS - Conference Proceedings Archive · 2008
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCycle ergometerTrunkBicycle ergometerPhysical medicine and rehabilitationAthletesPhysical therapySimulationMathematicsMedicineComputer scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Simulation of sporting activities for the purpose of assessing physiological parameters and for conditioning athletes has been an important development within the sports world. The purpose of this study was to compare Olympic flatwater canoeing technique to that of an ergometer developed by Pyke et al. at Dalhousie University. The comparison, using three national team members, was both physiological and biomechanical in order to determine; 1) if accurate physiological measurements focusing on the upper body during racing conditions could be matched while using the laboratory ergometer; 2) if the ergometer movement patterns closely approximated the actual on-water racing stroke. The results indicated that the techniques were similar physiologically and different biomechanically. VE and VO2 max, for the 500 m. race and for a simulated 500 m. trial were close and consistent across all S's. Results for the 1000 m. were acceptable, but not as accurate as the 500 m. The use of the Pyke ergometer was judged on the whole to be a valid physiological testing procedure. The major difficulty with the ergometer was that it forced all S's to alter their racing strokes in order to successfully maintain movement of the mechanism. Changes in movement and velocity patterns of the trunk, arms and hands of all S's were considerable and led to the conclusion that this ergometer, in its original design, not be used as a training device.

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 score0.561
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.070
GPT teacher head0.317
Teacher spread0.246 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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