Importance of weightlifting performance analysis in anti-doping
Bibliographic record
Abstract
We examined the potential roles of the athlete's performance passport (APP) for doping detection by analyzing the relationship between weightlifting performance and sanction status. For the present study, performance data of 'not-sanctioned' (26740 datasets) and 'sanctioned' (289 datasets) male athletes were acquired from the website of the International Weightlifting Federation (www.iwf.net). One-way ANOVA, correlation analysis, and t-tests were used to analyze the relationship between athletes' use of doping and their performances across age and body weight. Athletic performance was significantly greater for athletes in the sanctioned group than those of the same age group who were not sanctioned, and this performance difference between the two groups was the greatest in their late thirties at 20.6% (not-sanctioned 292.0kg vs. sanctioned 352.3kg) (p < 0.05). From the age group analysis, out of 289 sanctioned cases, 84 cases, which was the largest proportion, were found within the top 10-25% of their performances. When stratified by body weight, athletic performance was significantly greater for the sanctioned group than the not-sanctioned group, and this performance gap was the greatest in the bodyweight category of 96 at 18.6% (not-sanctioned 310.1kg vs. sanctioned 367.8kg) (p < 0.05). From the body weight category analysis, out of 289 sanctioned cases, 75 cases, which was the largest proportion, were found within the top 10-25% of their performances. Additionally, the mean difference in performance between not-sanctioned and sanctioned groups was the largest in the body weight category of 67kg in the ages of 15-19 at 20% (not-sanctioned 234.6kg vs. sanctioned 281.5kg). These results are interpreted to mean that in male weightlifters 1) sanctioned athletes were detected in all ranges of performances regardless of age and body weight, 2) there were even higher rates of sanctioned athletes who performed within the top 10-25% of each age group and body weight category, 3) there were significant differences in performance between not-sanctioned and sanctioned group for all body weight categories, excluding +109, in the ages of 15-19 and 20-24, 4) therefore, performance data can be effectively used to better target suspected athletes for doping testing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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".