Vertical jumping power normative-reference values: Utilizing a large cohort of Canadian University students
Bibliographic record
Abstract
Abstract Study aim: This study aimed to develop vertical jumping power normative-reference standards based on a large cohort of Canadian university students. Material and methods: Data were collected from a sample of 960 male and female participants, aged 20 to 29 years (mean body mass index [BMI]: 24.44± 3.80). Participants performed the Sargent jump-and-reach test using a wall-mounted vertical jumping height scale, where vertical jump distance (VJD) was determined by subtracting standing reach height from vertical jumping reach height. An independent samples t-test was conducted to compare the means of all variables (i.e., absolute power, relative power, and VJD) and to test for statistical significance between sexes. Results: Data from all variables were higher ( P < 0.05) among male participants, including absolute power (W), relative power (W·kg −1 ), and VJD (m), and statistically significant differences between sexes were noted. Collected data facilitated the creation of sex-specific normative-reference standards including percentile rankings and seven performance classifications. Conclusions: These norms will be instrumental in supporting the convenient and practical evaluation of vertical jumping power performance data and may even reduce the need for other, more exhaustive testing methods (e.g., Wingate Anaerobic Test).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".