Bibliographic record
Abstract
The increasing involvement of young children in intense physical training over the past several decades has generated concerns as to its potential effects on children’s growth and maturation. Puberty in humans is characterized by large hormonal changes resulting in both physical and sexual maturation. Since intense training prior to puberty, together with the potential metabolic effects of dieting, can alter hypothalamic-pituitary function, the time at which athletic training is initiated has been implicated as a factor in delayed menarche and sexual maturation in female athletes. On the other hand, some studies have suggested that delayed menarche is likely due to genetic factors. Girls who mature later often self-select or are recruited by coaches into sports that favor small or very lean bodies. Body composition has also been used to explain both delayed menarche and menstrual irregularities observed among elite athletes. A higher prevalence of menstrual dysfunction has been reported for adolescent athletes participating in weight-dependent sports as compared to that observed in other sports. However, as recently suggested, there is no direct cause-effect association between fatness and reproduction and, in actual fact, energy availability, and not body fat, regulates reproductive function in females. More research is warranted to further investigate this interaction between short-term changes in fuel availability and athletic amenorrhea in female adolescents. It is concluded that, given the many factors that have been shown to influence menarche and menstruation, the role played by physical training alone as a causative factor in the later onset of puberty and menstrual irregularities in active young females is still unclear. Research involving longitudinally designed studies is required to identify whether the maturity differences observed between female athletes and non-athletes are the result of nature or nurture, and what the balance between the two factors is.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".