Origin of the Fastest 5 km, 10 km and 25 km Open-Water Swimmers—An Analysis from 20 Years and 9819 Swimmers
Bibliographic record
Abstract
In elite pool swimmers competing at world class level, mainly athletes from the United States of America and Australia are dominating. Little is known, however, for the nationality of dominating swimmers in elite open-water long-distance swimming races such as the official FINA races over 5 km, 10 km and 25 km-held since 2000. The aim of this study was to investigate the participation and performance trends by nationality of these elite open-water swimmers. Race results from all female and male swimmers competing in 5 km, 10 km and 25 km FINA races between 2000 and 2020 were analyzed. A total of 9819 swimmers competed between 2000 and 2020 in these races. The five countries that figure most times among the top ten in 5 km, 10 km and 25 km races over the years were Italy, Germany, Russia, Brazil and the Netherlands. In 10 km races, considering the all the athletes from each country, male athletes from Germany, Italy, and France presented faster race times than the other countries. In 10 km, female athletes presented no significant difference among the countries. In 5 and 25 km races, there were no differences between countries, for male and female athletes. Moreover, comparing only the 10 best results (top 10) from each country, there were no differences between countries in 5 km, 10 km and 25 km, for male and female athletes. Men were faster than women for all three distances. In summary, male swimmers from Europe (i.e., Germany, Italy, France) are dominating the 10 km FINA races. In the 5 km and 25 km FINA races, there is no dominating nationality, but among the top five countries in the top 10 over the years, three are European countries.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".