Training Principles and Issues for Ultra-endurance Athletes
Bibliographic record
Abstract
Ultra-endurance competition is defined as events that exceed than 6 hours in duration. The longer events rely on long-term preparation, sufficient nutrition, accommodation of environmental stressors, and psychologic toughness. Successful ultra-endurance performance is characterized by the ability to sustain a higher absolute speed for a given distance than other competitors. This can be achieved through a periodized training plan and by following key principles of training. Periodization is an organization of training into large, medium and small training blocks which are referred to as macro-, meso-, and microcycles, respectively. When the sequencing of training is correctly applied, athletes can achieve a high state of competition readiness and during the months of hard training, avoid the overtraining syndrome. A plan is executed in accordance with the following principles of training: all-around development, overload, specificity, individualization, consistent training, and structural tolerance. Training relies heavily on the athlete's tolerance to repetitive strain. Today's ultra-endurance athlete must also follow appropriate nutritional practices in order to recover and prepare for daily training and remain injury free and healthy. Rehydration after exercise, together with the timing and method of increased food intake to cope with heavy training, are essential for optimal performance. Furthermore, the treatment of soft tissue after training or racing is necessary to control inflammation.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".