Contextual factors on physical demands in professional women's soccer: Female Athletes in Motion study
Bibliographic record
Abstract
Abstract The aim of this study was to examine the impact of contextual factors on relative locomotor and metabolic power distances during professional female soccer matches. Twenty‐eight players (forwards, n = 4; midfielders, n = 12; defenders, n = 12) that competed in a 90‐min home and away match (regular season only). The generalised estimating equations (GEE) was used to evaluate relative locomotor and metabolic power distances for three contextual factors: location (home vs. away), type of turf (natural vs. artificial), and match outcome (win, loss and draw). No differences were observed for home vs. away matches. Moderate‐intensity running (20.0 ± 1.0 m min −1 and 16.4 ± 0.9 m min −1 ), high‐intensity running (8.6 ± 0.4 m min −1 and 7.3 ± 0.4 m min −1 ) and high‐metabolic power (16.3 ± 0.5 m min −1 and 14.4 ± 0.5 m min −1 ) distances were elevated on artificial turf compared to natural grass, respectively. Relative sprint distance was greater during losses compared with draws (4.3 ± 0.4 m min −1 and 3.4 ± 0.3 m min −1 ). Overall physical demands of professional women's soccer were not impacted by match location. However, the elevation of moderate and high‐intensity demands while playing on artificial turf may have implications on match preparations as well as recovery strategies.
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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) | 0.002 | 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".