The Reliability of Measuring Gross Efficiency During High-Intensity Cycling Exercise
Bibliographic record
Abstract
PURPOSE: To evaluate the reliability of calculating gross efficiency (GE) conventionally and using a back extrapolation (BE) method during high-intensity exercise (HIE). METHODS: A total of 12 trained participants completed 2 HIE bouts (P1 = 4 min at 80% maximal aerobic power [MAP]; P2 = 4 min at 100%MAP). GE was calculated conventionally in the last 3 minutes of submaximal (50%MAP) cycling bouts performed before and after HIE (Pre50%MAP and Post50%MAP). To calculate GE using BE (BGE), a linear regression of GE submaximal values post-HIE were back extrapolated to the end of the HIE bout. RESULTS: BGE was significantly correlated with Post50%MAP GE in P1 (r = .63; P = .01) and in P2 (r = .85; P = .002). Reliability data for P1 and P2 BGE demonstrate a mean coefficient of variation of 7.8% and 9.8% with limits of agreement of 4.3% and 4.5% in relative GE units, respectively. P2 BGE was significantly lower than P2 Post50%MAP GE (18.1% [1.6%] vs 20.3% [1.7%]; P = .01). Using a declining GE from the BE method, there was a 44% greater anaerobic contribution compared with assuming a constant GE during 4-minute HIE at 100%MAP. CONCLUSION: HIE acutely reduced BGE at 100%MAP. A greater anaerobic contribution to exercise as well as excess postexercise oxygen consumption at 100%MAP may contribute to this decline in efficiency. The BE method may be a reliable and valid tool in both estimating GE during HIE and calculating aerobic and anaerobic contributions.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".