<b>Determination of “Fat<sub>max</sub>” with 1 h cycling protocols of constant load</b>
Bibliographic record
Abstract
Several earlier studies were aimed at determining an exercise intensity that elicits maximal fat oxidation (Fatmax). However, these studies employed few different intensities or used exercise periods of too short a duration. All investigators described intensity with reference to maximal ergometric values, which might lead to metabolically inhomogeneous workloads between individuals. The aim of this study was to determine Fatmax by overcoming these methodological shortcomings of earlier investigations. Ten healthy recreational athletes (29 +/- 5 y; 75 +/- 6 kg; 1.81 +/- 0.04 m) conducted an initial incremental cycling test to determine VO2 peak (59.2 +/- 6.1 mL.min-1.kg-1) and individual anaerobic threshold (IAT; 221 +/- 476 W). Within 4 weeks, 5 constant-load tests of 1 h duration were carried out at 55%, 65%, 75%, 85%, and 95% IAT. During all tests indirect calorimetry (MetaMax I, Cortex, Leipzig, Germany) served to quantify fat oxidation. Capillary blood sampling for lactate measurements was conducted every 15 min. All subjects remained in a lactate steady state during the constant load tests, which minimized influences from excess CO2. There was no difference between the 5 intensities for the percentage of energy from fat metabolism (p = 0.12). Additionally, the intensities led to similar absolute amounts of oxidized fat (p = 0.34). However, there was a significant increase in fat metabolism with increasing exercise duration (p = 0.04). It is impossible to define one theoretical optimal intensity for fat oxidation that is true in all individuals. It is thus mandatory to perform an individual assessment with indirect calorimetry. Intra-individual day-to-day variation might render the use of several tests of long duration less applicable than incremental testing with stages of sufficient duration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 0.001 |
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".