Determination of Submaximal and Maximal Training Zones From a 3-Stage, Variable-Duration, Perceptually Regulated Track Test
Bibliographic record
Abstract
PURPOSE: To validate a new perceptually regulated, self-paced maximal oxygen consumption field test (the Running Advisor Billat Training [RABIT] test) that can be used by recreational runners to define personalized training zones. DESIGN: In a cross-sectional study, male and female recreational runners (N = 12; mean [SD] age = 43 [8] y) completed 3 maximal exercise tests (2 RABIT tests and a University of Montreal Track Test), with a 48-hour interval between tests. METHODS: The University of Montreal Track Test was a continuous, incremental track test with a 0.5-km·h-1 increment every minute until exhaustion. The RABIT tests were conducted at intensities of 11, 14, and 17 on the rating of perceived exertion (RPE) scale for 10, 5, and 3 minutes, respectively, with a 1-minute rest between efforts. RESULTS: The 2 RABIT tests and the University of Montreal Track Test gave similar mean (SD) maximal oxygen consumption values (53.9 [6.4], 56.4 [9.1], and 55.4 [7.6] mL·kg-1·min-1, respectively, P = .722). The cardiorespiratory and speed responses were reliable as a function of the running intensity (RPE: 11, 14, and 17) and the relative time point for each RPE stage. Indeed, the oxygen consumption, heart rate, ventilation, and speed values did not differ significantly when the running time was expressed as a relative duration of 30%, 60%, or 90% (ie, at 3, 6, and 9 min of a 10-min effort at RPE 11; P = .997). CONCLUSIONS: The results demonstrate that the RABIT test is a valid method for defining submaximal and maximal training zones in recreational runners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".