Vývoj počtu účastníků a vhodnost počtu postupových míst na CrossFit Open 2011–2017
Bibliographic record
Abstract
CrossFit is an all-round training program focused on the development of overall fitness. To win the title “Fittest on Earth”, the participants must pass a 3-round elimination system (CrossFit Open, Regions, Games). The objective of the study is to analyze the development of participants’ numbers in 2011-2017, men (n=1 012 297) and women (n=702 011), in 1st elimination round and to assess the appropriateness of the number of qualifying places in particular regions. The research data from public sources show a growing linear evolutionary trend in the number of participants (R2=0.98–0.99); the number of men was higher than the number of women through the whole period. In the category Individual Men (IM), the highest number of participants (n=40 716; i.e. 0.006 % of the region population) was registered in the region Europe in 2017; the lowest number of participants (n=3 678, i.e. 0.028 % of the region population) was found in West Canada in 2017. In the category Individual Women (IW), the highest number of participants (n=21 742, i.e. 0.003 % of the region population) was registered in Europe in 2017; the lowest number of participants (n=3 596, i.e. 0.027 %) was found in West Canada in 2017. The lack of good correspondence in the numbers of qualifying places was found in the IM category in 2016 and 2017; in the IW category, a good correspondence was found in the whole observed period 2011-2017. The odds ratio test has shown that in 2017, males from West Canada had 12.22 times bigger chance for qualifying than males from Latin America while females from the same regions had 7.03 times bigger chance for qualifying. In view of the results, it is possible to recommend an adjustment of the number of qualifying places and the division of the region Europe.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".