The effects of block training on pacing during 20-km cycling time trial
Bibliographic record
Abstract
The aim of this study was to determine the effects of block training (BL) on pacing during a 20-km hilly cycling time trial (TT) in trained cyclists. Twenty male cyclists were separated into 2 groups: control and BL. The training of each cyclist was monitored during a period of 3 weeks. In the first week cyclists performed an overload period of 7 consecutive days of high-intensity interval training followed by 2 weeks of normal training. Cyclists performed 1 TT before intervention and 2 TT after 7 and 14 days at the end of training. Each training session consisted of 10 sets of 3 repeated maximal-effort sprints (15, 30, and 45 s) with an effort/recovery duration ratio of 1:5. The main finding of this study was that the power output displayed a significantly higher start from the start until the halfway point of the TT (p < 0.05). Additionally, power output was characterized by a significant higher end spurt in the final 2 km in the BL after 2 weeks at the end of training (p < 0.05). In addition, after 2 weeks at the end of the overload period the distribution of cadence was significantly lower throughout the TT (p < 0.01). Therefore, a short period of consecutive days of intense training enhances cycling performance and changes the power output in the beginning and final part of the TT in trained cyclists.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".