INFLUENCE OF ENVIRONMENTAL FACTORS ON PEDAGOGICAL ASPECTS OF THE ACTIVE LIFEOF HIGHLY QUALIFIED ATHLETES
Bibliographic record
Abstract
The influence of environmental factors on physical education and sports has not lost its relevance. In order for the educational and training process to be carried out taking into account the real natural situation, we studied the opinion of highly qualified athletes on the influence of the surrounding natural environment on the effectiveness of their activities in the educational and training, pre-competition and competitive periods, analyzed and summarized information on the identified problem in existing literary sources. The questionnaire we developed with questions that have an ecologically oriented focus was offered to highly qualified athletes to study their opinion. The results of the questionnaire survey of members of the national teams of the Republic of Belarus in martial arts on their attitude to environmental factors at various stages of sports training revealed that the most favorable for the respondents are educational and training sessions in suburban sports complexes compared to city ones. The effectiveness of natural factors affecting highly qualified athletes during the period of active physical training has been established. It has been revealed that a favorable ecological environment contributes to the achievement of high sports results and is a prerequisite for the successful activity of athletes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".