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Record W2475759944 · doi:10.1080/02640414.2016.1213412

Pacing in age-group freestyle swimmers at The XV FINA World Masters Championships in Montreal 2014

2016· article· en· W2475759944 on OpenAlexaboutno aff
Pantelis Τ. Nikolaidis, Beat Knechtle

Bibliographic record

VenueJournal of Sports Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesPsychologyGroup (periodic table)Physical therapyDemographyMedicineSociologyPhysics

Abstract

fetched live from OpenAlex

Pacing strategies have been investigated for elite-standard freestyle swimmers, but little is known about pacing in age-group freestyle swimmers. We investigated changes in swimming time across distances in 4,481 women and men swimmers who competed in 100, 200, 400, and 800 m freestyle age groups from 25–29 years to 90–94 years in the FINA World Masters Championships 2014. In 100 to 800 m, there was a small lap×sex interaction (P < 0.001, 0.033 ≤ η2 ≤ 0.045) whereby women had larger lap-to-lap changes in swimming time than men. From 100 to 800 m, there were moderate to large lap×age group interactions (P < 0.001, 0.054 ≤ η2 ≤ 0.235), i.e., pacing patterns differed by age groups. There were small main effects of lap on time in 100, 200, 400 and 800 m freestyle events (P < 0.001, 0.033 ≤ η2 ≤ 0.045). In summary, (i) the largest increase in swimming time occurred during the second lap and a decrease in time occurred during the last lap, except in the 100 m, and (ii) the effect of participants’ sex on lap time indicated larger percentage changes of pacing in women than in men. These findings should help coaches to develop age- and event-tailored pacing strategies.

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)0.002
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 score0.019
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.276
Teacher spread0.244 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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