Evidence that skin suits affect long track speed skating performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it