Optimization of the Effort Preparation Process Among the Short Track Female Competitors in a Year Cycle
Bibliographic record
Abstract
The aim of conducted research was an attempt to define the dynamics of aerobic and anaerobic endurance changes in the short track female competitors training during a year cycle which is dependent on the energy characteristic of training burden. Nineteen female competitors of OMKŁS Opole club and KU AZS PO Opole, were put through the examination. Eight of them were members of National Team and Olympic Team (Vancouver 2010), moreover, the competitors participated in the World Cup, World Championship, European Championship. The other examined competitors were members of National Team. With the help of terrain and laboratory tests, an official record was made on every lap time, final time of every trial, HR max and HR medium as well as the concentration of lactate in blood in fourth minute after an effort. In the thirtieth minute, after an effort, HR and lactate concentration were registered in order to define the course of restitution. Anaerobic and mixed parameters were examined by the Wingate test in 7.5% load formula of body weight. The results of researches proved that a selection of applied training burdens was not conductive to the adaptation of process according to the Mathews, Fox model (1976). In the short track, a training burden should reflect the specific of an effort in this discipline, emphasising an anaerobic energy type in a year cycle of preparations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".