citrus search
A powerful similarity-based search engine for scientific literature that leverages machine learning to identify related research papers quickly and accurately.
About citrus search
Citrus is an advanced search engine designed to find related scientific publications through content and citation analysis. By selecting a key paper, users can explore related research efficiently, gaining insights into important contributions and the latest developments in their field. Citrus visualizes research trends over time and helps uncover relevant papers that may be overlooked by traditional search methods. Its core technology uses graph-based and text-based machine learning to measure the similarity between publications. Powered by Semantic Scholar's Open Research Corpus, Citrus indexes over 200 million articles and approximately 2 billion citations, making it a comprehensive tool for scientific exploration.
How to Use
1. Choose a seed paper relevant to your research. 2. Initiate the search or add additional seed papers for broader results. 3. Review related publications and research trends quickly.
Features
- Content and citation-based similarity analysis
- Visualization of research timelines
- Exploration of related scientific articles
- Machine learning-powered relevance ranking
Use Cases
- Discovering research papers similar to a key publication
- Gaining a comprehensive overview of a research area
- Identifying relevant work that traditional searches may miss
Best For
Pros
- Indexes an extensive database of scientific publications
- Provides visual timelines of key research contributions
- Utilizes advanced machine learning to assess paper similarity
- Enables discovery beyond keyword searches
Cons
- Results depend on the chosen similarity method (citation or content-based)
- Requires selecting an initial seed paper to begin the search
- Ongoing development may introduce occasional bugs