@article {deLaubell:2024:1525-4011:53,
title = "scite Revisited",
journal = "The Charleston Advisor",
parent_itemid = "infobike://annurev/tca",
publishercode ="annurev",
year = "2024",
volume = "25",
number = "4",
publication date ="2024-04-01T00:00:00",
pages = "53-56",
itemtype = "ARTICLE",
issn = "1525-4011",
eissn = "1525-4003",
url = "https://annurev.publisher.ingentaconnect.com/content/annurev/tca/2024/00000025/00000004/art00012",
doi = "doi:10.5260/chara.25.4.06",
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 are presented with a measure
of confidence, with relevant snippets of context from the citing article, and with access links (when available). scite includes a generative AI feature, called assistant, which translates natural language queries into searches, summarizes results, and provides relevant references. Users can
browse or visualize results using the web interface. Use cases include citation searching to conduct systematic reviews, to track the evolution of research on a particular topic, to identify experts on a topic, and to calculate impact factor. Users can also upload papers to conduct a reference
check for retracted articles.",
}