Bibliographic record
Abstract
Dear friends, We have been publishing scientific articles about gymnastics for a dozen of years. 2020 will be marked by the Olympic Games in Tokyo (Japan). We expect excellent gymnastics and perhaps some new records even though gymnastics is normally not about records. We believe that many articles published here in the past have contributed to safer and better gymnastics. The present issue brings seven articles from different lines of gymnastics, including general gymnastics, rhythmics, artistic gymnastics and acrobatics. Their topis range from sociology, psychology, motor abilities and motor control to physical education. There authors are from Brazil, Greece, Canada, Germany, Slovenia and Spain. Anton Gajdoš drafted another article related to the history of gymnastics, refreshing our awareness of Nikolay Adrianov, an excellent Russian gymnast who marked the era between 1970 and 1980 and later as a coach in Russia and Japan. Special thanks to our reviewers whose diligent work has improved the quality of the published papers. The list of reviewers in 2019 is at the end of this issue. Just to remind you, if you quote the Journal, its abbreviation on the Web of Knowledge is SCI GYMN J. I wish you pleasant reading and a lot of inspiration for new research projects and articles,
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.206 | 0.121 |
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".