Workload demands in professional multi-stage cycling races of varying duration
Bibliographic record
Abstract
OBJECTIVE: To analyse and compare the workload exerted by professional cyclists in 5-day, 8-day and 21-day stage races (5-SR, 8-SR, 21-SR). METHODS: The study subjects were 30 professional cyclists competing in 10 5-SR, 5 8-SR and 5 21-SR. Heart rate (HR) was measured during the races and categorised into three intensity zones: Z1 (below the ventilatory threshold (VT)), Z2 (between VT and the respiratory compensation threshold (RCT)) and Z3 (above RCT). The training impulse (TRIMP) was calculated by multiplying the sum of the time spent in each zone by 1, 2 and 3, respectively. Monotony (average TRIMP/SD) and strain (total TRIMPxmonotony) were also calculated for each race type. RESULTS: The average time spent in Z3 during each stage was significantly (p<0.05) higher for 5-SR ( approximately 31 min) and 8-SR ( approximately 28 min) than for 21-SR ( approximately 14 min). Daily TRIMP values in 5-SR ( approximately 400) and 8-SR ( approximately 395) were also higher than in 21-SR ( approximately 370). Monotony was similar across races ( approximately 3) but strain was about three times higher for 21-SR than for 5-SR and 8-SR. CONCLUSIONS: The cyclists' effort by stage was less for 21-SR than for 5-SR and 8-SR. Competition strain and monotony accumulated during longer races influence the choice of strategies adopted by cyclists. It is likely that the intensity of each stage is modulated by total race duration, with longer races averaging the lowest daily workload.
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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) | 0.001 | 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".