Diet and Nutraceuticals for Mental and Physical Performance in Athletes
Bibliographic record
Abstract
This supplement stresses the importance of diet and selected nutraceuticals that may impact both the physical and mental performance of athletes.The past 2.5 years living with the coronavirus disease 2019 pandemic have taxed all of us mentally and possibly to a greater extent in the athletic population.While life in general has returned to some form of normalcy for many, emerging from this pandemic has alerted us to the importance of vaccines and physical-distancing measures, especially as they relate to sport.The return of organized sport at all levels has also underscored the important roles that exercise and sport play in the lives and mental health of everyone, including athletes.The goal of this supplement is to provide recent information that will help athletes achieve optimal physical and mental performance in their chosen sport.The Gatorade Sports Science Institute has been bringing sports nutrition and sports science researchers together for ~ 37 years to discuss many topics that relate to the nutrition, performance, and well-being of athletes.Since 2012, these meetings have been known as the Gatorade Sports Science Institute Expert Panel.The worldwide coronavirus disease 2019 pandemic necessitated that the latest meeting in October of 2021 was again held in a virtual format.However, the meeting was a great success and following the meeting,
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".