Shout! letting it all out : effects of grunting on power performance
Bibliographic record
Abstract
In professional tennis today, there are more players grunting at higher and higher decibels. The current research investigates if grunting is beneficial in power movements based on the theory that it facilitates a forced exhalation by acting as a timing mechanism. Grunting has been demonstrated to give competitors an advantage in the sport of tennis but has not been examined in other sport contexts (Sinnett & Kingstone, 2010). The current research investigates if grunting can improve jumping and throwing distance. Performance measurements were taken on all tasks, left and right grip strength, vertical jump, medicine ball chest pass and standing broad jump, for grunting and non-grunting conditions. The results for the medicine ball chest pass and standing broad jump demonstrated that the means for the grunting condition (M = 4.77, SD = 1.25; M = 2.14, SD = .41) were significantly greater than the means for the non-grunting condition (M = 4.61, SD = 1.17, t(138) = -3.24 ; M = 2.07, SD = .41, t(135) = -5.96, p < .05) with small and medium effect sizes for the medicine ball chest pass and the standing broad jump (d = -.27 and -.51). Grunting was found to affect distance performance by improving the distance thrown or jumped when completing a medicine ball chest pass or a standing broad jump.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".