Italians Are the Fastest 3000 m Open-Water Master Swimmers in the World
Bibliographic record
Abstract
Background: It is well known that athletes originating from a specific region or country can master specific sports disciplines (e.g., East-African runners in long-distance running). In addition, physical and athletic performance are the result of an interaction between genetic, environmental and epigenetic factors. However, little is known about on what determines sports success and performance for long-distance master swimmers such as origin. The aim of the study was to investigate the participation and performance trends of elite master open-water swimmers competing in the World Championships (WC) in 3000 m open-water swimming between 1986 and 2019. Methods: A total of 9247 valid participants were analyzed using generalized linear models (GLMs) with a gamma probability distribution and log link function. Resultsː Most of the starters were from Italy (1646 participations), followed by the United States of America (USA) (1128 participations) and Germany (959 participations). Swimmers from Italy were significantly faster than swimmers from Canada, Germany, USA, Great Britain and also from all other countries grouped (p < 0.005). The age group from 35–39 years old was significantly faster than athletes from age groups of 25–29 years old, 30–34 years old, 40–44 years old, 45–49 years old and 50–54 years old (p < 0.005). The percentage of local athletes in WC was 36% and varied from 36% (Italy, 2004) to 43 % (Germany), 53% (Italy, 2012) and up to 68 % (USA, 1992). Conclusions: Swimmers from Italy were the faster and the most numerous starters during this period of 27 years and 15 editions all over the world in 3000 m master open-water 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".