Bibliographic record
Abstract
The human body is designed for movement and capable of incredible athletic and sporting achievements.Attempts to improve the ability to perform on the athletic stage are never-ending and involve a serious dedication to training.Athletes, coaches, support personnel, and scientists are constantly experimenting with new ways to improve training and its applicability to performance.Nutrition is clearly a major factor in the success of training and performance as fuel is needed to power the human engine.The amount, type, and timing of nutritional intake play a large role in the physical and mental success of the athlete.In addition, nutritional intake also influences the adaptation to training and the recovery from training, to positively impact performance.The Gatorade Sports Science Institute (GSSI) brought together researchers for a meeting in February 2013 to discuss the recent evidence that nutrition influences athletic training and performance.Following the meeting, the authors were asked to summarize the recent work in their research area, resulting in the manuscripts in this Sports Medicine supplement.A major step forward in sports nutrition has been the ability to more thoroughly understand how training alters the body at the molecular level, in both acute and chronic situations.Various forms of training have been shown to activate cell signalling pathways that ultimately produce protein-specific adaptations geared to improving sportspecific performance.Whether engaging in classic moderate intensity-high volume endurance training, high
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.027 |
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".