Technical-Methodological Report: A Nomogram for Peak Leg Power Output in the Vertical Jump
Bibliographic record
Abstract
Leg power is an important component in assessing both performance-related and health-related fitness. The Lewis equation and nomogram have been used for years to estimate leg power. A recent evaluation of the Lewis equation and further research led to the development of the Sayers equation. This equation provides an estimate of peak leg power, which has greater relevance than average power. Our purpose was to provide a simple and effective nomogram for calculating peak leg power output. The Sayers equation was transformed to an alignment nomogram and evaluated for facility of use and accuracy. The resultant alignment nomogram is easy to use and generates values for peak leg power in the vertical jump, which are well within the precision of the regression equation (r > 0.9999, CV < 0.2%). Interobserver error was less than 0.3% with a correlation of 0.9999. The Keir nomogram provides a simple and effective representation of the Sayers equation for use in both performance-related and health-related fitness assessments.
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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 | 0.053 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".