Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The purpose of this investigation was to assess the effects of alterations in taper components on performance in competitive athletes, through a meta-analysis. METHODS: Six databases were searched using relevant terms and strategies. Criteria for study inclusion were that participants must be competitive athletes, a tapering intervention must be employed providing details about the procedures used to decrease the training load, use of actual competition or field-based criterion performance, and inclusion of all necessary data to calculate effect sizes. Datasets reported in more than one published study were only included once in the present analyses. Twenty-seven of 182 potential studies met these criteria and were included in the analysis. The dependent variable was performance, and the independent variables were the decrease in training intensity, volume, and frequency, as well as the pattern of the taper and its duration. Pre-post taper standardized mean differences in performance were calculated and weighted according to the within-group heterogeneity to develop an overall effect. RESULTS: The optimal strategy to optimize performance is a tapering intervention of 2-wk duration (overall effect = 0.59 +/- 0.33, P < 0.001), where the training volume is exponentially decreased by 41-60% (overall effect = 0.72 +/- 0.36, P < 0.001), without any modification of either training intensity (overall effect = 0.33 +/- 0.14, P < 0.001) or frequency (overall effect = 0.35 +/- 0.17, P < 0.001). CONCLUSION: A 2-wk taper during which training volume is exponentially reduced by 41-60% seems to be the most efficient strategy to maximize performance gains. This meta-analysis provides a framework that can be useful for athletes, coaches, and sport scientists to optimize their tapering strategy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it