Physiological correlates of 2-mile run performance as determined using a novel on-demand treadmill
Bibliographic record
Abstract
The purpose of this study was to assess the reproducibility of an on-demand motorised treadmill to measure 2-mile (3.2 km) race performance and to examine the physiological variables that best predict this free-running performance in active men. Twelve men (mean (SD): age, 28 (9) years; stature, 1.79 (0.05) m; body mass, 72 (9) kg) completed the study in which maximum oxygen uptake (VO2 max), running economy, and running speedin the abstract section. They appear in the rest of the paper.), running economy, and running speed at VO2 max (vVO2 max), lactate threshold (vLT), and 4 mmol.L-1 fixed blood lactate concentration (v4) were measured. Subsequently, the maximal lactate steady state (MLSS) was identified using a series of 30-min treadmill runs. Finally, each participant completed a 2-mile running performance trial on 2 separate occasions, using an on-demand treadmill that adjusts belt speed according to the participant's position on the moving belt. The average 2-mile run speed was 15.7 (SD, 1.9) km.h-1, with small individual differences between repeat-performance trials (intraclass correlation coefficient = 0.99, 95% CI 0.953 to 0.996; standard error of measurement as coefficient of variation = 1.5%, 95% CI 1.0% to 2.5%). Bivariate regression analyses identified VO2 max, vVO2 max, VO2 (mL.kg-1.min-1) at MLSS, vLT, v4, and velocity at MLSS (vMLSS) as the strongest individual predictor variables (r2 = 0.69 to 0.87; standard error of the estimate = 1.08 to 0.72 km.h-1) for 2-mile running performance. The vLT and vMLSS explained 85% and 87% of the variance in running performance, respectively, suggesting that there is considerable shared variance between these parameters. In conclusion, the on-demand treadmill system provided a reliable measure of distance running performance. Both vLT and vMLSS were strong predictors of 2-mile running performance, with vMLSS explaining marginally more of the variance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".