Knowledge of repetitions range affects force production in trained females.
Bibliographic record
Abstract
Most studies have examined pacing strategies with cyclical activities (running and cycling). It has been demonstrated that males employ different pacing strategies during repeated maximal voluntary contractions (MVCs) dependent upon a known endpoint. Since different fatiguing mechanisms have been identified between the genders, it is not known if females use comparable pacing strategies. The purpose of this study was to examine if informing female subjects regarding the number of MVCs to perform would affect force and electromyography (EMG). Twenty well-trained females completed 3 fatiguing protocols in a randomized order. In the control condition participants were informed they would perform twelve MVCs and then actually completed twelve. In the unknown condition they were not told how many MVCs to perform but were stopped after twelve. In the deception condition they were initially informed to perform 6 MVCs, but after the 6(th) MVC they were asked to perform a few more MVCs and were stopped after twelve. During the first 6 MVCs, forces in the deception condition were greater compared to the unknown (p = 0.021, ES = 0.65, 5%) and control (p = 0.022, ES = 0.42, 3%) conditions. No differences were found between conditions in the last 6 MVCs. A main effect for repetitions showed force deficits during the first 6 MVCs (p = 0.000, ES = 1.81, 13%) and last 6 MVCs (p = 0.05, ES = 0.34, 3%). No differences were found between conditions in biceps and triceps EMG. However, EMG decreased during the first 6 MVCs for biceps (p = 0.001, ES = 1.0, 14%) and triceps (p = 0.001, ES = 0.76, 14%) across conditions. No differences were found in the last 6 MVCs. The anticipation of performing fewer MVCs led to increased force, whereas no endpoint led to decreased force production. Key pointsPacing strategies occur during repeated (fatiguing) MVCs as a function of end point expectations.Females use similar pacing strategies as previously published results with males.Without a known end point, females will tend to pace themselves by decreasing force output even when asked to perform maximal contractions.
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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 | 0.002 |
| 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) | 0.006 | 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".