The Effect of an Upper-Body Agonist-Antagonist Resistance Training Protocol on Volume Load and Efficiency
Bibliographic record
Abstract
The objective of this study was to investigate the acute effects on volume load (VL) (load × repetitions) of performing paired set (PS) vs. traditional set (TS) training over 3 consecutive sets. After a familiarization session 16 trained men performed 2 testing protocols using 4 repetition maximum loads: TS (3 sets of bench pull followed by 3 sets of bench press performed in approximately 10 minutes) or PS (3 sets of bench pull and 3 sets of bench press performed in an alternating manner in approximately 10 minutes). Bench pull and bench press VL decreased significantly from set 1 to set 2 and from set 2 to set 3 under both the PS and TS conditions (p < 0.05). Bench pull and bench press VL per set were significantly less under TS as compared to PS over all sets, with the exception of the first set (bench pull set 1) (p < 0.05). Session totals for bench pull and bench press VL were significantly less under TS as compared to PS (p < 0.05). Paired set was determined to be more efficient (VL/time) as compared to TS. The data suggest that a 2-minute rest interval between sets (TS), or a 4-minute rest interval between similar sets (PS), may not be adequate to maintain VL. The data further suggest that PS training may be more effective than TS training in terms of VL maintenance and more efficient. Paired set training would appear to be an efficient method of exercise. Practitioners wishing to maximize work completed per unit of time may be well advised to consider PS training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".