Effects of resistance training using known vs unknown loads on eccentric‐phase adaptations and concentric velocity
Bibliographic record
Abstract
The aims of this study were to compare both eccentric‐ and concentric‐phase adaptations in highly trained handball players to 4 weeks of twice‐weekly rebound bench press throw training with varying loads (30%, 50% and 70% of one‐repetition maximum [1‐ RM ]) using either known ( KL ) or unknown ( UL ) loads and to examine the relationship between changes in eccentric‐ and concentric‐phase performance. Twenty‐eight junior team handball players were divided into two experimental groups ( KL or UL ) and a control group. KL subjects were told the load prior each repetition, while UL were blinded. For each repetition, the load was dropped and then a rebound bench press at maximum velocity was immediately performed. Both concentric and eccentric velocity as well as eccentric kinetic energy and musculo‐articular stiffness prior to the eccentric‐concentric transition were measured. Results showed similar increases in both eccentric velocity and kinetic energy under the 30% 1‐ RM but greater improvements under 50% and 70% 1‐ RM loads for UL than KL . UL increased stiffness under all loads (with greater magnitude of changes). KL improved concentric velocity only under the 30% 1‐ RM load while UL also improved under 50% and 70% 1‐ RM loads. Improvements in concentric movement velocity were moderately explained by changes in eccentric velocity ( R 2 =.23‐.62). Thus, UL led to greater improvements in concentric velocity, and the improvement is potentially explained by increases in the speed (as well as stiffness and kinetic energy) of the eccentric phase. Unknown load training appears to have significant practical use for the improvement of multijoint stretch‐shortening cycle movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".