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Record W2169252273 · doi:10.1123/ijspp.3.1.31

A Time–Motion Analysis of Elite Solo Synchronized Swimming

2008· article· en· W2169252273 on OpenAlexaffabout
Teresa Alentejano, Dru Marshall, Gordon J. Bell

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMathematicsVideo recordingAnimal scienceGeodesyStatisticsEnvironmental scienceGeographyBiologyComputer science

Abstract

fetched live from OpenAlex

PURPOSE: To determine the total amount and relative time periods of face immersion (FI) in a synchronized swimming solo routine and the relationship between FI, distance covered, and the technical-merit score of the 11 top Canadian soloists at a synchronized swimming national championship (mean age 20 +/- 1.8 y, height 173.3 +/- 4.1 cm, and body mass 58.3 +/- 4 kg). METHODS: Videotape and timing of solo performances combined with manual tracking of pool patterns. RESULTS: Analysis of performance revealed that an average of 18 FI periods, mean of 6.8 s, were performed for an average total time of 133.7 +/- 27.1 s (range 102.2 to 199.8 s). The average longest FI time period was 25.45 +/- 6.2 s (range 18.18 to 38.72 s), and most (10/11) of these were in the first third of the solo. The mean total horizontal distance covered was 57.61 +/- 6.84 m (range 48.61 to 68.2 m), and the total horizontal distance covered relative to time was 0.276 +/- 0.034 m/s (range 0.235 to 0.340 m/s). No significant relationships were found between any of the FI periods and the distance covered or between the technical-merit score and FI periods. Each solo contained 6 to 8 underwater sequences, none of which were longer than 40 seconds, the cutoff deemed dangerous by FINA (Fédération Internationale de Natation). CONCLUSION: This study shows that the times underwater for solos in Canada are within safety limits recommended by FINA and that judging in Canada is not related to underwater periods of swimming.

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 score0.208
Threshold uncertainty score0.215

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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

Citations20
Published2008
Admission routes2
Has abstractyes

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