Effect of Acute Fatigue and Training Adaptation on Countermovement Jump Performance in Elite Snowboard Cross Athletes
Bibliographic record
Abstract
Countermovement jump performance was examined in response to acute neuromuscular (NM) fatigue (study I) and chronic training (study II) in elite snowboard cross (SBX) athletes, through both typical (countermovement jump [CMJ]-TYP) and alternative (CMJ-ALT) CMJ variables. Seven (4 men and 3 women) elite (Olympic-level) SBX athletes participated in study I, and 5 of the same athletes (2 men and 3 women) participated in study II. Countermovement jump variables relating to force, velocity, power, and time were measured during both eccentric and concentric jump phases, with CMJ-TYP variables reflecting CMJ output and CMJ-ALT variables reflecting CMJ mechanics. In study I, CMJ performance was assessed before and after a fatiguing lower-body exercise protocol, and in study II, CMJ performance was examined before and after a 19-week structured training block. Meaningful differences in CMJ performance were examined using the magnitude of change (effect sizes [ES]) for group and individual changes. Acute fatigue decreased peak force and eccentric function, while the duration of the jump increased. The structured training block increased peak force and eccentric function, while jump duration markedly decreased. In both study I and study II, the largest ES were associated with CMJ-ALT variables. The CMJ test seems a suitable monitoring tool in elite SBX athletes for the detection of both acute fatigue and training-adaptation. Compared with CMJ output, CMJ mechanics exhibits more marked and divergent changes after both acute NM fatigue and a structured training block. CMJ-ALT variables should therefore be incorporated into CMJ analysis.
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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 | 0.000 | 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".