Covariance of Isometric and Dynamic Arm Contractions: Multivariate Genetic Analysis
Bibliographic record
Abstract
The purpose of the present study was to examine genetic and environmental contributions to individual differences in maximal isometric, concentric and eccentric muscle strength and muscle cross-sectional area (MCSA) of the elbow flexors. A generality versus specificity hypothesis was explored to test whether the 4 strength variables share a genetic component or common factors in the environment or whether the genetic/environmental factors are specific for each strength variable. The 4 variables under study were measured in 25 monozygotic and 16 dizygotic male Caucasian twin pairs (22.4 +/- 3.7 years). The multivariate genetic analyses showed that all 4 variables shared a genetic and environmental component, which accounted for 43% and 6% in MCSA (h2 = 81%), 47% and 20% in eccentric (h2 = 65%), 58% and 4% in isometric (h2 = 70%) and 32% and 1% in concentric strength (h2 = 32%) respectively. The remaining variation was accounted for by contraction type specific and muscle cross-sectional area specific genetic and environmental effects, which accounted for 38% and 14% in MCSA, 18% and 15% in eccentric, 12% and 26% in isometric and 0% and 67% in concentric strength respectively. This exploratory multivariate study suggests shared pleiotropic gene action for MCSA, eccentric, isometric and concentric strength, with a moderate to high genetic contribution to the variability of these characteristics.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".