Current status of genetic engineering in the fields of medicine, pharmacology and agriculture in China, Japan and Korea
Bibliographic record
Abstract
Research and development (R&D) of genetic engineering in Asian countries, particularly in China, Japan and Korea, have been achieved great success and applied in many aspects of the social economic fields. In the area of medicine, these nations focus on studying the diagnosis of cancers, infectious, and hereditary diseases using molecular techniques based on PCR, and next generation sequencing; implementing clinical trials by gene therapy and improving prevention with innovative vaccines such as subunit or DNA vaccines. In addition, preparatory studies on human cells or embryos utilizing CRISPR/Cas9 genome editing technology have been undertaken in hopes of finding new treatments for genetic and cancer diseases. In the field of agriculture, many Asian countries have carried out R&D and approved genetically modified crops and produtcs for releasing into the environment and utilizing for food, feed and processing. The modified traits mainly are insect resistance, virus resistance, herbicide tolerance, drought tolerance, salinity tolerance, and increased nutritional value. In general, the level of development and application of genetic engineering in these nations has outstripped in Asia, but still underdevelopment state compared to the United States, Canada and several European countries. Therefore, each country should have appropriate policies and investments to promote the application of advanced biotechnologies in the world in order to improve the quality of human life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".