Stratégie optimale d’amaigrissement dans les sports à catégories de poids
Bibliographic record
Abstract
Weight-class sports incite the sportsmen to loose weight to change class and increase their chance of success in competition. Although these sports are very demanding on the physiological level (high intensity), most of the competitors undertake caloric restrictions that are likely to induce physiological disorders detrimental to their health and sport performances. Two strategies allow to loose bodyweight. The first is maintained over a very short time (less than 1 week), the second is maintained over a longer period (several weeks). Managing weight reduction, food intake, and physical activities over several weeks is a particularly efficient way to conserve the sportsman's performance abilities. On the other hand, the transgression of certain principles in only one of these fields is enough to deteriorate the sportsman's capacities of performance and (or) his health, whatever the duration of the period of the loss of weight. During food restriction, the carbohydrate and protein rations must be increased to prevent the unavoidable involutions of body composition and performance. In spite of food restriction, the training intensity must be high, and only the training volume must decrease to remain competitive.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".