| Autor: |
Scott Spillias, Paris Tuohy, Matthew Andreotta, Ruby Annand-Jones, Fabio Boschetti, Christopher Cvitanovic, Joseph Duggan, Elisabeth A. Fulton, Denis B. Karcher, Cécile Paris, Rebecca Shellock, Rowan Trebilco |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Cell Reports Sustainability, Vol 1, Iss 7, Pp 100132- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2949-7906 |
| DOI: |
10.1016/j.crsus.2024.100132 |
| Popis: |
Summary: Systematic approaches to evidence synthesis can improve the rigor, transparency, and replicability of a literature review. However, these systematic approaches are resource intensive. We evaluate the ability of ChatGPT to undertake two stages of evidence syntheses (searching peer-reviewed literature and screening for relevance) and develop a collaborative framework to leverage both human and AI intelligence. Using a scoping review of community-based fisheries management as a case study, we find that with substantial prompting, the AI can provide critical insight into the construction and content of a search string. Thereafter, we evaluate five strategies for synthesizing AI output to screen articles based on predefined inclusion criteria. We find that low omission rates ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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