Peaking for optimal performance: Research limitations and future directions
Bibliographic record
Abstract
A key element of the physical preparation of athletes is the taper period in the weeks immediately preceding competition. Existing research has defined the taper, identified various forms used in contemporary sport, and examined the prescription of training volume, load, intensity, duration, and type (progressive or step). Current limitations include: the lack of studies on team, combative, racquet, and precision (target) sports; the relatively small number of randomized controlled trials; the narrow focus on a single competition (single peak) compared with multiple peaking for weekly, multi-day or multiple events; and limited understanding of the physiological, neuromuscular, and biomechanical basis of the taper. Future research should address these limitations, together with the influence of prior training on optimal tapering strategies, and the interactions between the taper and long-haul travel, heat, and altitude. Practitioners seek information on how to prescribe tapers from season to season during an athlete's career, or a team's progression through a domestic league season, or multi-year Olympic or World Cup cycle. Practical guidelines for planning effective tapers for the Vancouver 2010 and London 2012 Olympics will evolve from both experimental investigations and modelling of successful tapers currently employed in a wide range of sports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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".