A Single Test Protocol to Establish the Full Spectrum of Exercise Intensity Prescription
Bibliographic record
Abstract
PURPOSE: We aimed to test the extended capabilities of the SRS protocol by validating its capacity to predict the power outputs for targeted metabolic rates (V̇O 2 ) and time-to-task failure ( Tlim ) within the heavy- and severe-intensity domain, respectively. METHODS: Fourteen young individuals completed (i) an SRS protocol from which the power outputs at GET and RCP (RCP CORR ), and the work accruable above RCP CORR , defined as W ' RAMP , were derived; (ii) one heavy-intensity bout at a power output predicted to elicit a targeted V̇O 2 equidistant from GET and RCP; and (iii) four severe-intensity trials at power outputs predicted to elicit targeted Tlim at minutes 2.5, 5, 10, and 13. These severe-intensity trials were also used to compute the constant-load-derived critical power and W ´ ( W ' CONSTANT ). RESULTS: Targeted (2.41 ± 0.52 L·min -1 ) and measured (2.43 ± 0.52 L·min -1 ) V̇O 2 at the identified heavy-intensity power output (162 ± 43 W) were not different ( P = 0.71) and substantially concordant (CCC = 0.95). Likewise, targeted and measured Tlim for the four identified severe-intensity power outputs were not different ( P > 0.05), and the aggregated coefficient of variation was 10.7% ± 8.9%. The derived power outputs at RCP CORR (192 ± 53 W) and critical power (193 ± 53 W) were not different ( P = 0.65) and highly concordant (CCC = 0.99). There were also no differences between W ' RAMP and W ' CONSTANT ( P = 0.51). CONCLUSIONS: The SRS protocol can accurately predict power outputs to elicit discrete metabolic rates and exercise durations, thus providing, with time efficiency, a high precision for the control of the metabolic stimulus during exercise.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".