Study on the Charateristics of Women's 1000m Speed Skating's Speed Rhythm and the Strategy of ZHANG Hong Preparing for the Sochi Winter Olympics
Bibliographic record
Abstract
This paper,using the literature review,interview and mathematical statistics and other research methods,analyzes the speed rhythm of women's 1000mspeed skating in 2012—2013season which the athletes attend in Harbin,Japan,Canada and the United States cup around twice,explores the charateristics of women's 1000mspeed skating's speed rhythm and the main winning factors and the strategy of our national key skaters preparing for the Sochi Winter Olympics in.Conclusion:1)Speed rhythm feature of women's 1000mspeed skating is the use of fast paced start,the 0~200msegmentation result is the influencing factor which lead to the different speed rhythm when the athletes respectively starte from innrelane and outerlane,but this effect dose not have inevitability.2)The absolute speed and speed endurance are the main winning factors of the project,both are equally important,rather than people have considered speed endurance.3)The key strategy of ZHANG Hong preparing for the Sochi Winter Olympics is to increase performance in the sprint stage,specific means on the one hand,strengthened the strength exercise,on the other hand,improved the stability of her ankle and hip flexibility;Secondly,the adjustment of the outerlane's speed rhythm make ZHANG Hong's outerlane performance more stable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".