Soccer-related performance in eumenorrheic Tunisian high-level soccer players: effects of menstrual cycle phase and moment of day
Bibliographic record
Abstract
BACKGROUND: This study aimed to examine the combined effects of menstrual cycle phase and moment of day on female soccer players' performances in the five-jump test (5JT), the repeated shuttle-sprint ability test (RSSA), and the Yo-Yo intermittent recovery test level 1 (YYIRT1). METHODS: Eleven eumenorrheic Tunisian high-level soccer players volunteered to participate. Each subject individually participated in three testing periods: one in the early follicular phase (menses), one in the late follicular phase, and another in the luteal phase. In each period, two test sessions were conducted: one at 07:30 and another at 17:30. The testing routines included the 5JT, the RSSA, and the YYIRT1. RESULTS: None of the measured variables were altered due to menstrual cycle phase (all P>0.05). Mean time during RSSA was significantly lower in the afternoon session compared to the morning session (8.48±0.27 s and 8.77±0.34 s, respectively, P<0.001), while 5JT performance was significantly higher in the afternoon compared to the morning (9.08±0.58 m and 8.60±0.56 m, respectively, P<0.001). CONCLUSIONS: Soccer-specific endurance as well as jumping and repeated sprinting ability of Tunisian female high-level soccer players are not affected due to menstrual cycle phase neither in the morning nor in the afternoon.
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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".