Improving the Prediction of Maturity From Anthropometric Variables Using a Maturity Ratio
Bibliographic record
Abstract
PURPOSE: This study aimed to improve the prediction accuracy of age at peak height velocity (APHV) from anthropometric assessment using nonlinear models and a maturity ratio rather than a maturity offset. METHODS: The dataset used to develop the original prediction equations was used to test a new prediction model, utilizing the maturity ratio and a polynomial prediction equation. This model was then applied to a sample of male youth academy soccer players (n = 1330) to validate the new model in youth athletes. RESULTS: = 88.88%) within a general population of boys, particularly with relatively high/low APHVs. This study has also highlighted the successful application of the new model to estimate APHV using anthropometric variables in youth athletes, thereby supporting the use of this model in sports talent identification and development. CONCLUSION: This study argues that this newly developed equation should become standard practice for the estimation of maturity from anthropometric variables in boys from both a general and an athletic population.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".