Influence of Anthropometric Characteristics on Ice Swimming Performance—The IISA Ice Mile and Ice Km
Bibliographic record
Abstract
Ice swimming following the rules of IISA (International Ice Swimming Association) is a recent sports discipline starting in 2009. Since then, hundreds of athletes have completed an Ice Mile or an Ice Km in water colder than 5 °C. This study aimed to expand our knowledge about swimmers completing an Ice Mile or an Ice Km regarding the influence of anthropometric characteristics (i.e., body mass, body height, and body mass index, BMI) on performance. We analyzed data from 957 swimmers in the Ice Km (590 men and 367 women) and 585 swimmers in the Ice Mile (334 men and 251 women). No differences were found for anthropometric characteristics between swimmers completing an Ice Mile and an Ice Km although water temperatures and wind chill were lower in the Ice Km than in the Ice Mile. Men were faster than women in both the Ice Mile and Ice Km. Swimming speed decreased significantly with increasing age, body mass, and BMI in both women and men in both the Ice Mile and Ice Km. Body height was positively correlated to swimming speed in women in the Ice Km. Air temperature was significantly and negatively related to swimming speed in the Ice Km but not in the Ice Mile. Water temperature was not associated with swimming speed in men in both the Ice Mile and Ice Km but significantly and negatively in women in Ice Km. In summary, swimmers intending to complete an Ice Mile or an Ice Km do not need to have a high body mass and/or a high BMI to swim these distances fast.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".