Wingate normative-reference values for a large cohort of Canadian university students
Bibliographic record
Abstract
Abstract Study aim : The Wingate Anaerobic Test evaluates anaerobic power and capacity. Used to assess performance, historically among athletic populations, its evaluative capacity for individuals of varying athletic abilities is limited by a lack of normative data based on large participant populations. This study developed Wingate normative-reference values based on a large-scale cohort that is representative of the Canadian university student population. Material and methods : Data were collected from 872 participants, aged 20 to 29 years (mean body mass index [BMI]: 24.44 kg/m 2 ). Testing was completed on a cycle ergometer using a widely recognized protocol, with resistance set at 7.5% of participants’. An independent samples t-test was used to compared the means of dependent variables (i.e., peak power [PP], mean power [MP], and fatigue index fi) and test for statistical significance (p < 0.05) between sexes, and Cohen’s d determined effect size. Results : Males had higher PP and MP (W and W · kg −1 ), whereas females exhibited lower FI (%). Statistically significant differences between sexes were observed for all variables. Conclusions : Collected data yielded normative-reference standards, including percentile rankings and performance classifications. These norms will allow for significant practical applications, including an effective method to assess anaerobic performance and health.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".