Data mining and panoramic analysis on global Nipah virus-related patent applications
Bibliographic record
Abstract
Objective To describe the status of Nipah virus-related patent application around the world and to provide references for relevant patent application and researches in China. MethodsGlobal data on Nipah virus-related patent applications from 2002 up to August 18, 2020 were searched through Derwent Innovations Index of Web of Science. Derwent Data Analyzer 9.0 was adopted to conduct data mining and panoramic analysis on overall trend, technology field and layout, countries of registration, and protection intensity of the declared patents.ResultsFrom the year of 2002, the annual number of Nipah virus-related patent applications increased generally. The United States, China and Canada are among the major registration countries of those patent applications. The filed patent applications were mainly for antiviral agent, viral antigen or antibody products, DNA recombination technology, vaccine development, and virus detection method. The number of patent applications for antiviral agents/antivirals and viral antigen/antibody is relatively small in China compared to that in major registration countries. Conclusion The increase in annual number of Nipah virus-related patent applications indicates that technology research and development on the pathogen of biosafety level 4 are enhanced continuously in the United States and other countries and relevant researches need to be promoted in China for Nipah virus-related biosafety and epidemic prevention.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".