Estimating energy expenditure for brief bouts of exercise with acute recovery
Bibliographic record
Abstract
Four indirect estimations of energy expenditure were examined, (i) O(2) debt, (ii) O(2) deficit, (iii) blood lactate concentration, and (iv) excess CO(2) production during and after 6 exercise durations (2, 4, 10, 15, 30, and 75 s) performed at 3 different intensities (50%, 100%, and 200% of VO(2) max). Analysis of variance (ANOVA) was used to determine if significant differences existed among these 4 estimations of anaerobic energy expenditure and among 4 estimations of total energy expenditure (that included exercise O(2) uptake and excess post-exercise oxygen consumption, or EPOC, measurements). The data indicate that estimations of anaerobic energy expenditure often differed for brief (2, 4, and 10 s) bouts of exercise, but this did not extend to total energy expenditure. At the higher exercise intensities with the longest durations O(2) deficit, blood lactate concentration, and excess CO(2) estimates of anaerobic and total energy expenditure revealed high variability; however, they were not statistically different. Moreover, they all differed significantly from the O(2) debt interpretation (p < 0.05). It is concluded that as the contribution of rapid substrate-level ATP turnover with lactate production becomes larger, the greatest error in quantifying total energy expenditure is suggested to occur not with the method of estimation, but with the omission of a reasonable estimate of anaerobic energy expenditure.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".