Atualização sobre estimativas do gasto calórico de atletas: uso da disponibilidade energética
Bibliographic record
Abstract
Introduction and aim: Athletes present high daily caloric expenditure, requiring greater food intake and, sometimes, energy supplements. The energy needs of athletes are directly proportional to the type, frequency, intensity and duration of training. The difficulty of calculating the energy expenditure during physical activities lies in the energy demand being mixed, so it is important to know in which condition it is possible to measure or estimate the energy cost. Materials and methods: A review of the current literature on energy recommendations for athletes was carried out, comparing current Brazilian recommendations with the new North American proposal from Dietitians of Canada, the Academy of Nutrition and Dietetics, and the American College of Sports Medicine. Results and discussion: There are several equations for calculating energy estimates, with Harris-Benedict and Dietary Reference Intake (DRI) being the most used. In addition, there are recommendations for the energy intake of athletes proposed by the Brazilian Society of Sports Medicine. Recently, a new proposal appears in US guidelines, based on energy availability (ED), which is defined as the energy remaining after exercise for basic physiological processes. For DE, unlike the previous proposals, fat free mass (MLG) or lean mass (MM), instead of total body weight, is considered for energy quantification necessary to promote energy balance and optimum health of athletes. Conclusion: This new concept is useful for improving athlete performance by providing enough calories to maintain muscle mass.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".