Quantification of the manifestations of fatigue during treadmill running
Bibliographic record
Abstract
Abstract During whole‐body exercise, fatigue is difficult to quantify; however, changes to mechanical, physiological and psychological systems during exercise are associated with the development of fatigue. To quantify fatigue, one must therefore assess changes occurring in these variables. The purpose of this study was to demonstrate a method to assign weightings to selected variables and to combine them into a single value quantifying changes occurring during exercise. Twelve female recreational runners performed one hour of treadmill running, during which heart rate, respiration rate, stride frequency and six selected psychological variables were collected at defined intervals throughout the run. Data were normalised and a principle component analysis was performed. The resulting first eigenvector was termed the “contribution vector” and indicated the weighting of each variable towards the global exercise‐induced changes in the body. The projection of data onto the contribution vector resulted in a value described as the “fatigue index”. An assessment of the generalisation of the method to new data was performed using a leave‐one‐out cross‐validation procedure and indicated that the index is accurate to within 3.01% of the maximum index value measured. The method developed here has the advantage over current methods due to its multifactorial, causal and customisable nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".