Methodological Reconciliation of CP and MLSS and Their Agreement with the Maximal Metabolic Steady State
Bibliographic record
Abstract
PURPOSE: This study aimed to compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS. METHODS: After a ramp test, 10 recreationally active males performed four to five severe-intensity constant-power output (PO) trials to estimate CP and three to four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the three-parameter hyperbolic (CP3-hyp), two-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with the lowest combined parameter error identified the "best-fit" CP (CPbest-fit). MLSS was determined as an increase in blood lactate concentration ≤1 mM during constant-PO cycling from the 5th (MLSS5-30), 10th (MLSS10-30), 15th (MLSS15-30), 20th (MLSS20-30), or 25th (MLSS25-30) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady-state V˙O2 (MV˙O2ss). RESULTS: Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS15-30, MLSS20-30, and MLSS25-30. The PO at MV˙O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP2-hyp (244 ± 46 W) and CPlin (248 ± 46 W) and MLSS15-30 and MLSS20-30 (both 245 ± 46 W), respectively, displayed, on average, the greatest agreement with MV˙O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV˙O2ss. CONCLUSIONS: Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model estimate should be considered. For MLSS, MLSS15-30 and MLSS20-30 demonstrated the highest degree of accuracy.
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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 | 0.044 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".