Scientometric Analyses of International Scientific and Technological Collaboration in China Agricultural University
Bibliographic record
Abstract
To assess accurately the international scientific and technological collaboration productivity of China Agricultural University(CAU),based on the last ten year citations in Science Citation Index(SCI) under the Web of Science,the CAU international collaboration output,collaboration countries regions and collaboration fields,the paper analyzed the h-Index and highly cited papers,by use of the scientometric method.The result showed that the international collaborative research and development in CAU maintain a rapid level in recent ten years,and the international cooperation in basic research fields,such as soil science,ecology,agricultural science,biology,and so on,has achieved significant progress,but cooperation in engineering technology fields,which emphasizes on technology application,is lagging behind.Cooperation countries and regions trend to a multiplex pattern,but USA,Japan and Canada are still the main partners.In fact,the quantity and growth rate of the papers can not show the level of research,and it also fail to prove that the research has reached the international standards.So,we should pay attention to improve the quality of the SCI papers while pursuing the quantity.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.085 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".