What Results Achieve the Best Youth Athletes When They Became Seniors? Evidences from a Portuguese Female Artistic Gymnastics 40 Years’ Period
Bibliographic record
Abstract
The main purpose of this study was to examine the competitive pathways of the Portuguese female artistic gymnasts who achieved the first places in the earlier stages of their competitive categories, trying to understand how they were able or not to achieve the same level of competitive results over time. Further, it also was purpose of this study to analyze if there existed differences on that according to the level of performances reached by them at the beginning of their sport competitive careers. Therefore, were examined the sporting careers of all of the 282 female athletes who were classified at least once in the top six places of all national competitions organized by the Federation of Gymnastics of Portugal between 1971 and 2011 in the various existing categories (i.e., 10-11, 12-13, 14-15 and 16+ years old). Contrary to what it was found in most studies carried in other sports across several countries and sports, this study showed that a high percentage of the athletes who won the competitions at the beginning of their careers did it over again along their careers until the category of seniors, showing a high stability of the obtained results and a high longevity career. Therefore, it seems to suggest that the responsible persons by their sport training process (i.e., coaches and other club’s staff) are very efficacious in identifying the more talented female gymnasts and/ or to support them along their sporting careers, also stressing the importance of investigating the strategies adopted to achieve it.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".