Bibliographic record
Abstract
Introduction: As it is in a number of sports, in CrossFit, nutrition is critical for providing training load and faster re covery processes.Applied methodology and methods: We surveyed 12 men and 13 women training CrossFit unprofessionally.The average age of the men was 31.5 years, average sports experience of 3.6 years, and performing an average of 3.5 workouts per week.The average age of the women was 28.9 years, average sports experience of 2.7 years, and performing an average of 3.6 workouts per week.The subjects completed a diet-assessment questionnaire developed by us, which included questions about age, training experience, number of training sessions per week, height and weight and 28 questions about their weekly consumption of basic food products.Basic metabolite rate (BMR) was calculated according to the Harris-Benedict formulas.Daily energy intake (DEI) and daily energy needs (DEN) was calculated от BMR, multiplied by physical activity coefficient dependent on the number of weekly training sessions.Results: We estimated relative DEN of 34.0 kcal/kg BW and DEI of 37.4 kcal/kg of men.The DEN of women was 36.6 kcal/kg BW and the DEI was 38.8 kcal/kg BW.With regard to the intake of proteins, fats and carbohydrates, there are no significant differences between the two groups under study.Intake of fats of animal origin was slightly higher in males than those in the women.Conclusions: In the study groups, we see a good ration between DEN and DEI and a high relative protein intake and a lower intake of fat, characteristic more about power sports.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".