Bibliographic record
Abstract
Background/Study Context: Previous research on triathlon performance analyzed age trends for the Top Ten or Top Five finishers in world championship or national races at Olympic, Half-Ironman, and Ironman distances. The findings indicated higher age declines and/or earlier onset of decline in swimming and running than cycling. However, the designs of those studies took no account of possible differences between cross-sectional and longitudinal trends (i.e., cohort differences versus age changes). METHODS: This study analyzed performance times over the inaugural 5 years of the Half-Ironman world championship held in Clearwater, Florida, from 2006 to 2010. Only one previous study is known that examined age trends in performance for this triathlon distance. The data from the official race results showed 5549 age class competitors that provided 6541 sets of observations. Analyses by mixed linear modeling (MLM) partitioned the data to compare discrete and interactive cross-sectional and longitudinal trends for swimming, cycling, and running, respectively. RESULTS: The findings showed an historical decrease in cycling and running but not swimming times. Performance times were lower by men than women, with the gender discrepancy higher in some older age classes. Comparable to earlier findings for the Half-Ironman triathlon, cross-sectional performance decline was apparent for all triathlon activities from an early cohort age (i.e., 35-39 years). Although longitudinal trend showed significant gains for swimming, running, and overall times, interactions between cohort age and age change showed longitudinal decline that began at a younger cohort age for running (35-39 years) than swimming (50-55 years), but the interaction was nonsignificant for cycling. These interactions add to the knowledge about cohort differences and age changes in triathlon performance. CONCLUSIONS: Practical applications of the findings suggest that conservation of effort might explain the absence of longitudinal change in cycling performance at older cohort ages. The authors reason that increased effort in cycling might benefit overall times of older triathletes.
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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 | 0.001 |
| 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) | 0.003 | 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".