The Importance of Sailing Teaching in the Sailing Simulation Training Classroom-Taking the Sailing Course in Qingdao University of Science and Technology as an Example
Bibliographic record
Abstract
With the successful hosting of the 2008 Qingdao Olympic sailing regatta and the settlement of large and well-known events such as the Clipper Global Sailing Race in China, the Chinese sailing has been developing rapidly. At the same time, Chinese college students sailing has developed rapidly in recent years. The China University Student Sailing Championship in 2017 attracted participation from more than 20 universities, including Xiamen University and Tsinghua University. Qingdao University of Science and Technology, as the first university participating in sailing in China, has the only sailing simulation training classroom in China. Their sailing course is carried out smoothly and regularly and the course has been incorporated into the school’s teaching system, with professional syllabus design. For the past few years, the Qingdao University of Science and Technology Sailing Team has actively participated in domestic and international sailing events and achieved excellent results. The role of sailing simulation training classroom in teaching is particularly prominent, and after completing the basic knowledge of sailing and the simulated operation, students can go for the water exercise with half the effort. This paper researches on the importance of sailing simulation training classroom to the primary sailing teaching.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".