Evidence that skin suits affect long track speed skating performance
Bibliographic record
Abstract
Wind tunnel measurements of the aerodynamic drag ( Fd) of competitive speed skating suits were compared to the Olympic race results of athletes who wore these suits in three consecutive Winter Olympic games. A novel, multi-fabric speed skating race suit (SWIFTSkin) that was designed to reduce the Fd of long track skaters was first introduced at the 2002 Salt Lake City Olympics. This suit provided a 10.1% reduction in Fd over previous suits. Skaters from two countries wore the SWIFTSkin suit and won 16 of a possible 30 medals while setting 8 world records. On average, the Olympic performance of 59 skaters in the SWIFTSkin suit exceeded their previous personal best performance by 1.03%. A similar performance analysis of skaters from other nations clad in single fabric speed suits exhibited minor differences between pre-Olympic and Olympic performances. For subsequent Olympic games, the SWIFTSkin was worn by skaters from up to six nations while skaters competing for other nations wore suits that were designed with similar features. At the 2006 Torino and 2010 Vancouver Olympics, the difference in pre-Olympic to Olympic performance based on type of suit worn diminished for all skaters. The aerodynamic benefits of the SWIFTSkin measured in a wind tunnel coupled with the initial step change in performance noted with the introduction of the SWIFTSkin into competition and the reduction in the advantage provided by this apparel as its design features were assimilated into general Speed Skating competitive apparel provide observational evidence that apparel can impact elite sport performance.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".