Determining anthropometry related with Fencingusing social data mining
Bibliographic record
Abstract
This paper presents a study to determine the size and direction of changes in anthropometric characteristics between two female teams of players Fencing belonging to the Juarez City University and Universite Quebecoise Au Montreal, considering assess differences in anthropometric parameters, body fat, body mass index (BMI) and body density induced by sport−specific morphological optimization (adaptation). The survey included a total of 160 male Fencing players, all members of University teams of Fencing. The sample from Juarez City consisted of 95 players (71.9% of target population) aged between 18 and 30 years, and the sample from Montreal included 65 players (50% of target population) aged between 19 and 29 years. The variables of influence, in the Fencing to be considered for the development of this study have been described and measured under standard conditions by procedures established by the International Biological Program. They measured 23 anthropometric variables influence reflecting basic human body characteristic described by skeletal bone lengths (total leg length, total arm length, hand length, foot length, and height), breadths (hand at proximal phalanges, foot in metatarsal area, biacromial, biliocristal, biepicondylar femur, biepicondylar humerus, and radio−ulnar wrist breadth), girths (chest, arm, forearm, thigh, and calf girth), skinfold thickness as a measure of subcutaneous adiposity (triceps, subscapular, axillary, calf, and abdominal skinfold thickness), and mass. Additionally, estimates of body mass index (BMI), body density, and percentage of body fat were calculated from the primary measures to reveal possible trends in adiposity measures and the human body. Key words: Anthropometrics, social data mining, and influencing variables in fencing.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".