Estimating peak oxygen uptake based on postexercise measurements in swimming
Bibliographic record
Abstract
To assess the validity of postexercise measurements in estimating peak oxygen uptake (V̇O2peak) in swimming, we compared oxygen uptake (V̇O2) measurements during supramaximal exercise with various commonly adopted methods, including a recently developed heart rate - V̇O2 modelling procedure. Thirty-one elite swimmers performed a 200-m maximal swim where V̇O2 was measured breath-by-breath using a portable gas analyzer connected to a respiratory snorkel, 1 min before, during, and 3 min postexercise. V̇O2peak(-20-0) was the average of the last 20 s of effort. The following postexercise measures were compared: (i) first 20-s average (V̇O2peak(0-20)); (ii) linear backward extrapolation (BE) of the first 20 s (BE(20)), 30 s, and 3 × 20-, 4 × 20-, and 3 or 4 × 20-s averages; (iii) semilogarithmic BE at 20 s (LOG(20)) and at the other same time intervals as in linear BE; and (iv) predicted V̇O2peak using mathematical modelling (pV̇O2(0-20)]. Repeated-measures ANOVA and post-hoc Bonferroni tests compared V̇O2peak (criterion) and each estimated value. Pearson's coefficient of determination (r(2)) was used to assess correlation. Exercise V̇O2peak(-20-0) (mean ± SD 3531 ± 738 mL·min(-1)) was not different (p > 0.30) from pV̇O2(0-20) (3571 ± 735 mL·min(-1)), BE(20) (3617 ± 708 mL·min(-1)), or LOG(20) (3627 ± 746 mL·min(-1)). pV̇O2(0-20) was very strongly correlated with exercise V̇O2peak (r(2) = 0.962; p < 0.001), and showed a low standard error of the estimate (146 mL·min(-1), 4.1%) and the lowest mean difference (40 mL·min(-1); 1.1%). We confirm that the new modelling procedure based on postexercise V̇O2 and heart rate measurements is a valid and accurate procedure for estimating V̇O2peak in swimmers and avoids the estimation bias produced by other methods.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".