Anthropometric and Physical Qualities of International Level Female Rugby Sevens Athletes Based on Playing Position
Bibliographic record
Abstract
Agar-Newman, DJ, Goodale, TL, and Klimstra, MD. Anthropometric and Physical Qualities of International Level Female Rugby Sevens Athletes Based on Playing Position. J Strength Cond Res 31(5): 1346-1352, 2017-The purpose of this study was to profile international level female sevens athletes and determine whether anthropometric and physical qualities are able to differentiate between backs and forwards. Twenty-four subjects with a mean (±SD) age of 22.8 ± 4.0 years and body weight of 69.4 ± 5.2 kg were sampled from a national team training program, ranked in the top 3 on the IRB Women's Sevens World Series. Anthropometric (height, body mass, and sum of 7 skinfolds) and performance measures (power clean, front squat, bench press, neutral grip pull-up, 40-m sprint, and 1,600-m run) were collected across the 2013-2014 centralized period and compared across playing position. The 13 backs (mean age ± SD = 21.3 ± 3.5 years) and 11 forwards (mean age ± SD = 24.5 ± 4.0 years) had significant differences in body mass (66.40 ± 3.48 vs. 72.87 ± 4.79 kg) and initial sprint momentum (366.8 ± 19.8 vs. 399.2 ± 22.4 kg·m·s). However no other measures showed positional differences. The lack of positional differences in female rugby sevens may be due to the multifarious physical requirements of a sevens athlete, leading to a generic athletic profile, or perhaps due to a lack of selective pressure. Also, it is conceivable that the anthropometric and physical qualities measured in this study lacked the necessary precision or failed to capture the unique attributes of each position. In conclusion, this is the first investigation profiling international level female sevens athletes. The normative data presented within this article highlight the physical requirements of female sevens athletes for strength and conditioning practitioners. In addition, the lack of positional differences discovered should impact the training program design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".