Defining Running Intensity Domains from Critical Speed Derived from a 3-Minute All-Out Running Test
Bibliographic record
Abstract
Background/Objectives: This study examines the utility of the 3-min all-out test (3MT) in determining exercise intensity domains through critical speed (CS), offering a practical alternative to traditional VO2max-based methods. Methods: Eighteen trained runners completed both the 3MT and a graded treadmill test. CS, VO2max, and HRmax were measured and compared to markers such as ventilation threshold (VT), gas exchange threshold (GET), lactate threshold (LT), onset of blood lactate accumulation (OBLA), CS, and VO2max, which are threshold markers for defining six exercise intensity domains. Results: Results demonstrate that CS reliably distinguishes among six exercise intensity domains with greater precision and less variability than heart rate (HR) or VO2-derived metrics. Notably, HR was unable to differentiate between high-intensity domains, highlighting its limitations for performance prescription. Compared to VO2max testing, the 3MT offers a simpler, non-invasive, and time-efficient approach to identifying training zones and monitoring performance. CS effectively delineated moderate-to-heavy, heavy-to-severe, and severe-to-extreme intensity domains, with strong correlations to performance thresholds. The findings emphasize the accessibility and reliability of the 3MT, particularly in overcoming the logistical challenges of traditional testing methods. Conclusions: This research underscores the potential of the running 3MT as a valuable tool for individualized training prescription and performance monitoring in both research and applied sports contexts, paving the way for a broader adoption in athletic training methodologies.
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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 | 0.003 |
| 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) | 0.001 | 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".