Effects of a high-intensity swim test on kinematic parameters in high-level athletes
Bibliographic record
Abstract
The present study aimed to investigate the effects of a high-intensity swim test among top-level swimmers on (i) the spatial and temporal parameters of both the stroke and the 3-D fingertip pattern and (ii) the mechanical, muscular, and physiological parameters. Ten male international swimmers performed a 4 x 50 m swim at maximal intensity. Isometric arm flexion force with the elbow at 90 degrees (F90 degrees ), EMG signals of right musculus biceps brachii and triceps brachii and blood lactate concentrations were recorded before and after the swim test. Kinematic stroke (stroke length, rate, and velocity) and spatiotemporal parameters of the fingertip trajectory were measured by two underwater cameras during the first and last 50 m swims. After the swim test, F90 degrees and mean power frequencies of the EMG decreased significantly when blood lactate concentration increased significantly, attesting the reaching of fatigue. From the first to the last 50 m, stroke rate, stroke velocity, and temporal parameters of the fingertip trajectory exhibited significant increases although stroke length and spatial fingertip trajectory remained unchanged. General and individual adaptations were observed among the top-level swimmers studied. The present findings could be useful for coaches in evaluating fatigue effects on the technical parameters of swimming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".