Проблемы подготовки конькобежцев к выступлению в командной гонке преследования на олимпийских играх
Bibliographic record
Abstract
The article considers the problems of preparation and performance of the Russian national speed skating team in the team pursuit within the pre-Olympic season. We analyzed the results of performances of the male and female national teams of Russia at the world Championships by the single distances (2011-2013) prior to the Olympic tournament in 2014. The reasons that led to the unsuccessful performance in the team pursuit at the world Championships in Sochi 2013 have been discussed. The performance of the main rivals of the Russian team – national teams of the Netherlands, Canada, Germany and Korea has been considered. The problems associated with recruiting of the teams, selection of the athletes to participate in the team pursuit, taking into account their sports specialization, have been studied. The analysis showed that for the successful performance of the speed skaters in the pursuit race at the Olympic tournament the objective approach to the recruitment of the team is needed. Recommendations, implementation of which will allow counting on successful performance of the Russian skaters at the home Olympic Games have been given.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".