Effect of the Pacing Strategies on the Open-Water 10-km World Swimming Championships Performances
Bibliographic record
Abstract
Purpose: To (1) compare the pacing strategies of different-level open-water swimmers during the 10-km race of the FINA 2015 Swimming World Championships and (2) relate these pacing strategies to the race performance. Methods: Final and intermediate split times, as well as intermediate race positions, from the 10-km race participants (69 men and 51 women) were collected from the public domain and were divided into 5 groups (G1–G5) depending on their finishing positions. Results: Medalists and finalists (G1 and G2, respectively) presented an even pacing profile with swimming velocities similar to those of the less successful swimmers (G3–G5) on the initial and middle stages of the race but a 1.5–3% increase in swimming velocity in the last quarter of the race. This acceleration toward the end of the race, or “end spurt,” was largely related to the race performance and was not observed in the G3 and G4 (even-paced profile) or G5 (positive pacing profile) groups. Intermediate race positions and lap rankings were negatively related to finishing position, indicating a delayed positioning of the most successful swimmers at 25%, 50%, and 75% of race distance. Conclusions: The adoption of a conservative starting strategy by open-water swimmers with a negative pacing profile and delayed partial positioning seems to increase the chances of overall race success, as it allows a fast end spurt that is closely related to successful finishing race positions.
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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 | 0.002 |
| 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".