The Effects Of Prior Swimming And Cycling On Cardiorespiratory Variables In Highly-trained Female Sprint-distance Triathletes
Bibliographic record
Abstract
PURPOSE To determine the performance and cardiorespiratory effects of prior swimming and cycling on the sprint-distance triathlon run in female age-group triathletes. METHODS Eight highly-trained female age-group triathletes (21–38 years) having completed a sub-2:31 Olympic-distance triathlon in the previous year were selected for the study. Each performed a run VO2max and a cycle. VO2max test plus three experimental run conditions as 1) 25-minute maximal run (MR), 2) maximal sprint distance triathlon (ST) [15-minute swim, 40-minute cycle, 25-minute run (STR)], and 3) 25-minute run at the pace determined from ST (ATR). Oxygen consumption (VO2), heart rate (HR), blood lactate (LA), and ratings of perceived exertion (RPE) were measured at 5, 15, and 25 minutes while heart rate was recorded throughout. RESULTS The subjects had a mean VO2max running of 53.0 ± 3.9mL/kg/min and a mean VO2max cycling of 49.1 ± 4.7. No statistically significant differences were observed between the three run performances (p>0.05) on HR (absolute, %max, %threshold), LA, VO2 (absolute, %max, %threshold), and RPE. A significant difference was found at the three timing points (5, 15, 25 minutes) for HR (p <0.01), LA (p <0.01), VO2 (p <0.05), and RPE (p <0.01). During all runs, subjects worked between 80 and 90% of VO2max with HR over 90% of maximum at 15 and 25 minutes. There was no significant difference in distance covered during MR and STR (p <0.05). CONCLUSION The run portion of a sprint-distance triathlon is not adversely affected by the prior swim and cycle. The short duration of sprint-distance triathlons allows highly-trained athletes to perform at a high percentage of VO2max (84.4%) during the run segment.
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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".