Human performance across the extreme- and severe-intensity domains: Insights from decremental and priming exercise protocols.
Bibliographic record
Abstract
PURPOSE: To investigate whether W' in the extreme-intensity domain is smaller, yet linked to the W' predicted by the severe-intensity time series. METHODS: Twelve recreationally active participants (four females) completed 1) three extreme-intensity and three severe-intensity constant-power output (PO) trials to establish the PO duration series and to obtain W' within their respective domains (W'EXT and W'SVR, respectively); 2) two decremental protocols from extreme-to-severe (EXT1→SVR3) and from severe-to-severe POs (SVR2→SVR3); 3) one extreme- and one severe-intensity constant-PO trial preceded by priming exercise (EXT1P and SVR2P, respectively); and 4) control extreme- and severe-intensity constant-PO trials. Peak values for oxygen uptake (V̇O2peak), blood lactate concentration ([La-]b-peak), and minute ventilation (V̇Epeak) were also analyzed. RESULTS: W'EXT was significantly smaller than W'SVR (P < 0.001). There was no difference in W' between the composite EXT1→SVR3 and SVR2→SVR3 and SVR3 alone (all P > 0.05). Priming-induced increase in W'EXT and W'SVR was not different (P = 0.401). V̇O2peak, V̇Epeak, and [La-]b-peak were all greater in EXT1P compared with EXT1 (all P < 0.05). CONCLUSIONS: We showed that W'EXT is smaller than W'SVR during cycling. Following task failure during EXT1, more work could be performed at SVR3 until complete depletion of W'SVR. Additionally, heavy-intensity priming exercise increased W'EXT and W'SVR by a similar magnitude. Collectively, these findings suggest that performance within the extreme-intensity domain is limited by mechanisms, at least in part, different from those that limit performance within the severe-intensity domain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".