Effects of weight divisions in time-motion of female high-level Brazilian Jiu-jitsu combat behaviors
Bibliographic record
Abstract
Coaches and psychologists can use time-motion analysis to elaborate specific interventions for female BJJ athletes, increasing specific training context and reducing unnecessary psychological and physical demands and injuries. Therefore, the present study aimed to analyze high-level BJJ female athletes in the 2020 Pan-American Games by comparing the weight categories on the time-motion analysis. The time-motion analysis (i.e., approach, gripping, attack, defensive actions, transition, mounting, guard, side control, and submissions) of 422 high-level female BJJ combats was divided and compared by weight category as follows: Rooster (n = 8), Light Feather (n = 18), Feather (n = 122), Light (n = 84), Middle (n = 74), Medium Heavy (n = 44), Heavy (n = 36), Super Heavy (n = 36), using p ≤ 0.05. The main results indicated that the Super heavyweight category [3.1 (5.8;119.9) s] had a shorter gripping time than other weight categories, p ≤ 0.05. In contrast, roosters [7.2 (3.5;64.6) s] had longer gripping, transition [14.0 (4.8;29.6) s], and attack time [76.2 (27.7, 93.2)] than the light feather, middlers, and heavier weight categories, p ≤ 0.05. These findings should be considered for the psychological interventions and training prescription.
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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 | 0.001 |
| 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".