The International Teaching and Practice of Cryobiology and Biobankology Course in China
Bibliographic record
Abstract
In the past 10 years, clinical biobanks have experienced increasing expansion in China. Demand for systematically educated biobanking professionals is a priority for Chinese biobanks' agenda. The cryobiology and biobankology course is the first semester-long course in China, designed and developed at Central South University with international cooperation. Leading professors were from China, the United States, United Kingdom, and Canada to teach the latest version of biobanking knowledge and skills around the globe. This course is a comprehensive elective course with six specific teaching modules, which is suitable for graduate students majoring in basic medical sciences, clinical medicine, life sciences, mechanical engineering, and biomedical engineering, who would like to seek biobanking careers in the future. Participants from China, Czech Republic, Ghana, Madagascar, Tanzania, South Sudan, and Israel attended the course. Through taking this course, students can broaden their international academic horizons and cultivate the ability to learn and apply the knowledge of biology, medicine, and engineering to analyze and explain the low-temperature biology and clinical samples-based research practice. At the same time, the course enables students to realize the importance of multidisciplinary fields of biobanking and the significance of innovative precision medicine research, and further enlightens students' enthusiasm to pursue biobanking professional careers, and in the future they can proudly call themselves "biobankers."
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".