Explaining Performance in Elite Middle-Aged Runners: Contributions from Age and from Ongoing and Past Training Factors
Bibliographic record
Abstract
Researchers have contended that patterns of age-related decline are not necessarily due to age, but rather to disuse, or declining practice (Bortz, 1982; Ericsson, 2000; Maharam, Bauman, Kalman, Skolnik, & Perle, 1999). A regression approach was used to examine age and training variables as predictors of 10-km running performance between 40 and 59 years of age. A sample of 30 Masters runners (M age=50.1 years, M 10-km time=39:19) reported data for ongoing training, cumulative running in the past 5 years, and cumulative running earlier in a career. In Analysis 1, ongoing training variables explained more variance in performance than age alone, and reduced the unique variance attributable to age in a combined model. In Analysis 2, findings were replicated using past cumulative running variables and age; running in the past 5 years explained more unique variance than age alone. Discussion focuses on how findings relate to the selective maintenance account (Krampe & Ericsson, 1996), how various aspects of training help to preserve performance in aging populations, and recommendations for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".