Innovations in athletic preparation: Role of substrate availability to modify training adaptation and performance
Bibliographic record
Abstract
World records for athletic events continue to improve and in the search for superior methods to gain a competitive edge, coaches and athletes are constantly searching for the latest "magic bullet". Although it is assumed that optimal adaptation to the demands of repeated training sessions requires a diet that can sustain muscle energy reserves, this premise does not consider the unsolved longstanding question of whether it is a lack or a surplus of a substrate that triggers the training adaptation. As such, recent scientific enquiry has re-focused attention on the role of substrate availability before, during, and after training to amplify the training adaptation. There has also been a resurgence of interest in the potential for protein ingestion to improve performance and/or promote training-induced adaptations in skeletal muscle. Altitude training (real or simulated) is now an accepted part of competition preparation for many athletic events, and such interventions attract their own nutritional issues. These and other diet-training interactions with the potential to alter training adaptation and performance are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".