Efeito da idade relativa e sua relação com as características morfológicas e de desempenho em jovens futebolistas.
Bibliographic record
Abstract
In soccer, the relative age effect (RAE) was observed in both adult and young players. The RAE appears to be more pronounced in elite sports, probably by the need to select the best players to compete internationally. This study review: (1) the prevalence of RAE in soccer players, (a) considering competitive level (b) and specific position and (2) association between RAE (a) and anthropometric characteristics, (b) physical fitness components and technical skills. A total of 12 studies met all inclusion criteria for this review. One trial (meta-analysis) was included after the eligibility process. Overall, 77675 young soccer players were analysed. In all studies, significance level of 0.05 was set for the type I error. There is a consensus about the presence of an RAE in men’s soccer, and the percentage of players born in the first quarter in the selection year for professionals is high, with peak values found for elite young athletes, and a large decrease is evident throughout the regional and school representation. The relationship between RAE and the specific position is controversial, according to few studies. It is likely that players born in the first quarter differ in a variety of anthropometric characteristics and physical fitness components compared with peers born in the last quarter. Researchers need to understand the mechanisms by which RAE increase and decrease in order, to reduce and eliminate this social inequality that influence the experiences of athletes, especially in periods of development. Organizational and practical intervention is required.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".