Exercise intensity during repeated days of racing in professional triathletes
Bibliographic record
Abstract
The purpose of this study was to estimate the exercise intensity from the competition heart rate (HR) of professional triathletes during a multi-triathlon race. Five internationally ranked professional triathletes completed incremental cycling and running tests to assess the first and second ventilatory thresholds (i.e., VT and RCT) and the HR at VT and RCT. HR was then monitored during a 5 d multi-triathlon race: a prologue time trial (PTT, 0.2 km swim -- 5 km cycle -- 1.2 km run) that opened the race; short-distance triathlons (SHD; 1.3 km swim -- 36 km cycle -- 8.4 km run) performed on the 2nd and 5th days; and sprint-distance triathlons (SPD; 0.75 km swim -- 20 km cycle -- 5 km run) performed on the 3rd and 4th days. All trials except the last (i.e., the second SHD) were performed above HR corresponding to RCT. PTT elicited significantly higher mean HR than the other trials (except for the first SPD trial). In contrast, the last SHD elicited significantly lower HR than the other trials. These responses were globally similar in the 3 segments (i.e., swim, cycle, and run). This study demonstrates that the triathletes performed at very high intensity during a drafting-permitted multi-triathlon race. However, as shown for multi-day cycling distances, the HR responses depended on (i) the distance covered and (ii) group behavior.
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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".