Determination of Capacity and Rules of the Variability of Maximum Force Using Nonlinear Mathematical Models: a Case Study.
Bibliographic record
Abstract
The aim of this study is to determine the capacity and the variability of maximum force rules measured 1 RM for eight muscle groups (back-hip extensors, legs extensors, arm extensors, back extensors, shoulder and arms extensors, shoulder joint flexors, hip and knee extensors, trunk flexors). The determination was performed on the experimental results of the top basketball center player using repeated measurements and nonlinear mathematical models methods. Changes in maximum force were induced with 8 months of weight lifting training and analised with nonlinear regression analysis within 95% confidence interval. The results indicate that from all the models applied only the Asymptotic Regression, Michaelis-Menten and Gompertz Growth models had satisfactory performance and provided solid solutions to the given problem. This means that the models developed in this study properly and reliably determine the capacity and predicted changes in the maximum force (1 RM) for all eight monitored muscle groups.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".