ENERGY EXPENDITURE DURING BENCH PRESS AND SQUAT EXERCISES
Bibliographic record
Abstract
Despite the popularity of resistance training (RT), an accurate method for quantifying its metabolic cost has yet to be developed. We applied indirect calorimetry during bench press (BP) and parallel squat (PS) exercises for 5 consecutive minutes at several steady state intensities for 23 (BP) and 20 (PS) previously trained men. Tests were conducted in random order of intensity and separated by 5 minutes. Resultant steady state VO2 data, along with the independent variables load and distance lifted, were used in multiple regression to predict the energy cost of RT at higher loads. The prediction equation for BP was Y' = 0.132 + (0.031)(X1) + (0.01)(X2), R2 = 0.728 and S(xy) = 0.16; PS can be predicted by Y' = -1.424 + (0.022)(X1) + (0.035)(X2), R2 = 0.656 and S(xy) = 0.314; where Y' is VO2 X1 is the load measured in kg and X2 is the distance in cm. Based on a respiratory exchange ratio (RER) of 1.0 and a caloric equivalent of 5.05 kcal x L(-1), VO2 was converted to caloric expenditure (kcal x min(-1)). Using those equations to predict caloric cost, our resultant values were significantly larger than caloric costs of RT reported in previous investigations. Despite a potential limitation of our equations to maintain accuracy during very high-intensity RT, we propose that they currently represent the most accurate method for predicting the caloric cost of bench press and parallel squat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".