Effects of an increase in intensity during tapering on 1500-m running performance
Bibliographic record
Abstract
We examined the effect of completing the final interval training session during a taper at either (i) race pace (RP) or (ii) faster than RP on 1500-m running performance and neuromuscular performance. Ten trained runners (age, 21.7 ± 3.0 years; height, 182.9 ± 7.0 cm; body mass, 73.4 ± 6.8 kg; and personal best 1500-m time, 4:17.5 ± 0:26.9 min) completed 2 conditions consisting of 7 days of regular training and a 7-day taper, separated by 3 weeks of training. In 1 condition, the taper was prescribed using prediction models based on the practices of elite British middle-distance runners, with the intensity of the final interval session being equal to 1500-m RP. The taper was repeated in the high-intensity (HI) condition, with the exception that the final interval session was completed at 115% of 1500-m RP. A 1500-m treadmill time trial and measures of maximal voluntary contraction (MVC) and rate of force development (RFD) were completed before and after regular training and tapering. Performance was most likely improved after RP (mean ± 90% confidence limits, 10.1 ± 1.6 s), and possibly beneficial after HI (4.2 ± 12.0 s). Both MVC force (p = 0.002) and RFD (p = 0.02) were improved after tapering, without differences between conditions. An RP taper based on the practices of elite middle-distance runners is recommended to improve performance in young, subelite runners. The effect of this strategy with an increase in interval intensity is highly variable and should be implemented with caution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".