A Velocity-Related Means of Determining Resistance Load for the Wingate Test of Anaerobic Power
Bibliographic record
Abstract
This study compared power output during a 30-second max- imal cycle ergometry test using a resistance that was deter- mined to yield maximal 10-second power output (Windsor method) with power output generated using the more tra- ditional methods of resistance-setting determination (0.735 N·kg21 body mass (Wingate method) or 30-second power output at 49 N (Alberta method)). Sixteen men (mean 6 SD: 21.6 6 1.0 years of age, VO2 5 48.7 6 2.3 ml·kg 21 ·min 21 ) volunteered as subjects. Peak 1-second and best consecutive 5-second power outputs were not significantly greater across the 3 methods. Mean power output was significantly (p , 0.05) greater using the Windsor (621.0 6 18.2 W) or Wingate (588.8 6 23.5 W) resistance as compared with the Alberta (464.7 6 44.3 W) resistance. Fatigue index (FI) was greater using the Alberta method (42.67 6 4.21%) than when using either the Windsor (27.16 6 1.63%) or Wingate (28.98 6 3.05%) methods. These results indicate that the Alberta method fails to produce a resistance that yields maximal power output. Furthermore, the lack of significant difference between the Windsor and Wingate methods suggests that although the Windsor method results in a more ''individu- alized'' resistance, the Wingate method is equivalent with respect to maximal power output attainment and surpasses the Windsor method when one takes into consideration the time and effort involved in determining the Windsor resis- tance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".