Using Squat Testing to Predict Training Loads for Lower-Body Exercises in Elite Karate Athletes
Bibliographic record
Abstract
The purpose of this study was to determine the relationship between squat loads and 2 bilateral and 2 unilateral stepping lower-body exercises in predominantly unilateral movement elite athletes (Karate). Equations to predict loads for lower-body exercises based on the squat load were also determined. Fourteen male elite Karate athletes (age = 22.6 ± 1.2 years) performed 6 repetition maximum (RM) of the following free-weight bilateral exercises: back half squat, deadlift, leg press and unilateral stepping exercises, lunge; and step-up. Results showed that 6RM squat load was significantly (p < 0.001) correlated with deadlift (r = 0.86), leg press (r = 0.76), lunge (r = 0.86), and step-up (r = 0.92). Linear regression showed that the 6RM squat load was a significant predictor for deadlift, leg press, lunge, and step-up (R2 range from 0.57 to 0.85, p < 0.001). The following 6RM prediction equations were determined: (a) Deadlift = squat load (1.12)-16.60 kg, (b) Leg press = squat load (1.66) + 16.10 kg, (c) Lunge = squat load (0.61) + 9.39 kg, and (d) step-up = squat load (0.85)-10.36 kg. Coaches and fitness professionals can use the 6RM squat load as a time effective and accurate method to predict training loads for both bilateral and unilateral lower-body exercises with quadriceps as the prime mover. Load prescriptions for unilateral exercises should take into account the type of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".