Breath‐by‐breath pulmonary O<sub>2</sub> uptake kinetics: effect of data processing on confidence in estimating model parameters
Bibliographic record
Abstract
New Findings What is the central question of this study? In groups of young and older adults, we investigated whether techniques used as common practice for processing breath‐by‐breath pulmonary O2 uptake data from repeated step transitions in work rate into the moderate‐intensity exercise domain influence the model parameter estimations and confidence of describing the phase II pulmonary O2 uptake response. What is the main finding and its importance? Results demonstrate that regardless of age group, during transitions into the moderate‐intensity exercise domain, techniques for processing individual transitions did not affect parameter estimates describing the phase II pulmonary O2 uptake response; however, the confidence in the parameter estimation could be improved by the technique used to process individual trials. Abstract To improve the signal‐to‐noise ratio of breath‐by‐breath pulmonary O2 uptake ( ) data, it is common practice to perform multiple step transitions, which are subsequently processed to yield an ensemble‐averaged profile. The effect of different data‐processing techniques on phase II kinetic parameter estimates ( amplitude, time delay and phase II time constant (τ )] and model confidence [95% confidence interval (CI95)] was examined. Young (n = 9) and older men (n = 9) performed four step transitions from a 20 W baseline to a work rate corresponding to 90% of their estimated lactate threshold on a cycle ergometer. Breath‐by‐breath was measured using mass spectrometry and volume turbine. Mono‐exponential kinetic modelling of phase II data was performed on data processed using the following techniques: (A) raw data (trials time aligned, breaths of all trials combined and sorted in time); (B) raw data plus interpolation (trials time aligned, combined, sorted and linearly interpolated to second by second); (C) raw data plus interpolation plus 5 s bin averaged; (D) individual trial interpolation plus ensemble averaged [trials time aligned, linearly interpolated to second by second (technique 1; points joined by straight‐line segments), ensemble averaged]; (E) ‘D’ plus 5 s bin averaged; (F) individual trial interpolation plus ensemble averaged [trials time aligned, linearly interpolated to second by second (technique 2; points copied until subsequent point appears), ensemble averaged]; and (G) ‘F’ plus 5 s bin averaged. All of the model parameters were unaffected by data‐processing technique; however, the CI95 for τ in condition ‘D’ (4 s) was lower (P < 0.05) than the CI95 reported for all other conditions (5–10 s). Data‐processing technique had no effect on parameter estimates of the phase II response. However, the narrowest interval for CI95 occurred when individual trials were linearly interpolated and ensemble averaged.
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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 | 0.057 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".