Lower volume throughout the taper and higher intensity in the last interval session prior to a 1500 m time trial improves performance
Bibliographic record
Abstract
Eight highly trained middle-distance runners (1500 m personal best 4:01.4 ± 0:09.2 min) completed two 7-day tapers, separated by at least 3 weeks of regular training: (i) prescribed using prediction models from elite middle-distance runners, where continuous running volume was reduced by 30% and interval intensity was equal to 1500 m race pace (RP); and (ii) continuous running volume was reduced by 60% and intensity of the final interval session was completed at 110% of 1500 m race pace (HI). Performance was assessed using 1500 m time trials on an indoor 200 m track 1 day before, and 1 day after each taper. Performance time was improved after HI by 5.2 ± 3.7 s (mean ± 90% confidence limits, p = 0.03) and by 3.2 ± 3.8 s after RP (p = 0.15). The first and second 300 m segments of the 1500 m time trial were faster post-taper in RP (p = 0.012 and p = 0.017, respectively) and HI (both p = 0.012). Running faster than race pace late in a low-volume taper is recommended to improve 1500 m track performance. A positive pacing strategy is adopted after tapering, although care should be taken to avoid an over-fast start. Novelty: A large reduction in volume during tapering and an increase in final interval session intensity improves running performance. Athletes adopt a negative pacing strategy before tapering and a positive-pacing strategy after tapering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".