Effects of interindividual variation, state of training, and prolonged work on running economy
Bibliographic record
Abstract
The purpose of this study was to examine running economy differences between a group of well-trained runners and a group of non-runners. A secondary objective was to ascertain the effects of a prolonged run, near the ventilatory threshold, on running economy. Two groups of ten males [Mean±SD: age 25.6±4.8 yrs, 70.9±6.3 ml•kg-1•min-1 for the runners; age 20.6±2.3 yrs, 51.5±1.9 ml•kg-1•min-1 for the non-runners] performed 2 running economy tests (speeds = 2.68 m•s-1 and near Tvent) on 3 occasions prior to a prolonged run. Secondly, a prolonged run (maximum of 60 min) near the subject’s individual ventilatory threshold was performed and followed by 2 running economy tests at the same speeds. Despite the statistically significant difference in (p<0.05), the groups did not differ significantly in their running economy. As well, no statistically significant differences were found when running economy was measured as a function of distance (ml•kg-1•km-1) and when body mass was scaled to an exponent of 0.75 (ml•kg-0.75•min-1, ml•kg-0.75•km-1). The prolonged run had no statistically significant effects on the running economy of either group. The results from this study indicate, despite a marked difference in training status between the groups, there were no running economy differences. Further, the effects of a prolonged run near the ventilatory threshold were of insufficient duration and/or intensity to significantly perturb the running economy of either group.
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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) | 0.000 | 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".