MétaCan
Menu
Back to cohort
Record W4402615947 · doi:10.3390/jfmk9030167

A Comparison of Paddle Forces between Whitewater and Flatwater Training in C1 Canoe Slalom

2024· article· en· W4402615947 on OpenAlexafffund
James M. Wakeling, Stanislava Smiešková, Matej Vajda, Jan Bušta

Bibliographic record

VenueJournal of Functional Morphology and Kinesiology · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaVedecká Grantová Agentúra MŠVVaŠ SR a SAVSimon Fraser University
KeywordsPaddleTraining (meteorology)Physical medicine and rehabilitationMedicineEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: Becoming an elite canoe slalom athlete requires thousands of hours of training, spread over many years. It is difficult to assess the correct balance between flatwater and whitewater training because differences in the paddle forces on these terrains are not known. The aim of this study was to describe paddle forces during canoe slalom training on flatwater and whitewater courses for the C1 canoe category. METHODS: Paddle forces for twenty C1 canoe slalom athletes were quantified during all-out figure-of-eight tests on a flatwater course and during race simulations on a whitewater course. Paddle forces were measured using strain gauges embedded in the paddle shaft and quantified by their force, impulse, and stroke durations. RESULTS: The mean force during the pull phase of the paddle strokes was not significantly different between the flatwater and whitewater courses; however, the longer pull phase durations led to a greater pull phase impulse when paddling on the whitewater course. CONCLUSIONS: This study indicates that training for all-out runs on a whitewater course is more demanding for canoe slalom athletes than performing all-out trials on a flatwater figure-of-eight course. This evidence may help to develop effective training plans that are essential to reach the highest levels of the sport.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.263

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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Functional Morphology and KinesiologySame topicSports Performance and TrainingFrench-language works237,207