<Originals>The Effect of the Cycle Phase on Run Performance in an Ultraendurance Triathlon
Bibliographic record
Abstract
LAURSEN, P.B., THODES, E.C., LANGILL, R.H., McKENZIE, D.C. and TAUNTON, J.e., The Effect of the Cycle Phase on Run Performance in an Ultraendurance Triatholon. Adv. Exerc. Sports Physiol., Vol.7, No.3 pp.81-86, 2001. The purpose of this study was to investigate the effects of the cycle phase on the subsequent run performance in an ultraendurance triathlon. In addition, a secondary purpose was to examine the predicitive ability of VO2[?] and T[?] on an actual triathlon run performance. At first, ten highly trained male ultraendurance triathleters (x±SEM: age=36.5±1.5 yrs: body fat=12.2±1.2%) performed an incremental treadmill test (Tr1) to measure VO2[?] and T[?]. Secondly, a 5-h bike time trial (Bissp) was performed. and was immediately follwed by a second incremental treadmill test (Tr2). VO2[?] and T[?] were not significantly different between trials (x±SEM: VO2[?] -46.5±0.16 vs. 4.31±0.14 L-min VO2 at T[?]=3.35±0.11 vs. 3.15±0.11 L-min p>o.o5; Trl ws Tr2 respectvely). Furthermore, the T[?] estimation from Tr2 was not at a significantly reduced speed (x=SEM: Tr1 T[?]=8.8±0.2 mph. Tr2 T[?] -8.3±0.2 mph: p>0.05) and neither of these significantly correlated (r -0.473 to r -0.366: p>0.05 Tr1 to Tr2 respectively) with the actual Ironman Canada triathlon run pace (5.9±0.3 mph). Findings differ somewhat from recently reported Olympoic distance triatholon research. Prediction of run performance in an lrinman triathlon is difficult and likely the interaction of many confounding variables (such as fuel utilization and dehydration) not present to the same degree at the shorter distance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.021 |
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".