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Record W2198709123

CONCURRENT VERSUS DELAYED FEEDBACK: BIOMECHANICS IN ROWING

2013· article· en· W2198709123 on OpenAlexaboutno aff
Will George

Bibliographic record

VenueISBS - Conference Proceedings Archive · 2013
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsRowingVisual feedbackBiomechanicsPhysical medicine and rehabilitationAthletesPsychologyComputer sciencePhysical therapySimulationMedicineArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Biomechanical characteristics of rowers are often compared with ‘gold standard’ characteristics in order to identify improvable aspects of technique and to facilitate technique improvements. Biomechanical characteristics of athletes from the Canadian Women’s Under 23 Rowing team (n=8) were evaluated and two different methods of feedback were trialed to assess their effectiveness. Results showed 1 of 6 biomechanical characteristics to change significantly (p <.05) between trials. However, since boat speed (m/s) increased by 18.2% when using concurrent augmented feedback instead of a combination of visual and verbal delayed feedback, concurrent feedback was adopted by the team. Literature suggests this method would require alteration to be successful in different sports and concurrent feedback should be supplemented by delayed feedback in order for long term skill retention to occur.

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 score0.518
Threshold uncertainty score0.884

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 head0.036
GPT teacher head0.281
Teacher spread0.245 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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