@article {deLaubell:2023:1525-4011:60,
title = "Scite",
journal = "The Charleston Advisor",
parent_itemid = "infobike://annurev/tca",
publishercode ="annurev",
year = "2023",
volume = "24",
number = "4",
publication date ="2023-04-01T00:00:00",
pages = "60-63",
itemtype = "ARTICLE",
issn = "1525-4011",
eissn = "1525-4003",
url = "https://annurev.publisher.ingentaconnect.com/content/annurev/tca/2023/00000024/00000004/art00016",
doi = "doi:10.5260/chara.24.4.60",
author = "deLaubell, Lauren",
abstract = "scite is a next-generation citation index that uses machine learning to retrieve and contextualize scientific research. Citing works are categorized as mentioning, supporting, or contrasting based on an artificial intelligence (AI) analysis of rhetoric and presented with a measure of
confidence, a relevant snippet of context from the citing article, and access links (when available). Users can browse or visualize results using the web interface, install a browser extension to find similar citations for online text, or create a dashboard to visualize and track sets of citations.
Use cases include citation searching in order to conduct systematic reviews, track the evolution of research on a particular topic, identify experts on a topic, and calculate impact factor. Users can also upload papers to conduct a reference check for retracted articles.",
}