Identification of critical intensity from a single lactate measure during a 3-min, submaximal cycle-ergometer test
Bibliographic record
Abstract
We tested the hypothesis that critical intensity in cycling can be determined from a single delta blood lactate in the third minute of a submaximal cycle ergometer trial. Fourteen healthy young men performed four to six constant-power-output trials on a cycle ergometer to the limit of tolerance. Critical intensity was calculated via a linear model and subsequently validated. Lactate was measured at baseline and at 3 min from exercise onset. Delta lactate was the difference between these measures. Based on individual trials, we obtained the delta lactate–% validated critical intensity relationship and thereafter an estimate of critical intensity was computed. Validated and estimated critical intensity were compared by effects sizes, paired-sample t-test and Bland–Altman analysis. Delta lactate was a linear function of the intensity of exercise, expressed as % validated critical intensity (R2 = 0.89). Estimated critical intensity was not different from (d = 0.03, P = 0.98) and highly correlated with (R2 = 0.88) validated critical intensity. The bias between measures was 0.03 W (≠0) with a precision of 7 W. The results suggest that critical intensity in cycling can be accurately and precisely determined from delta lactate during a sub-maximal trial and so provides a practical and valid alternative to direct determination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".