Estimating the deep replicability of scientific findings using human and artificial intelligence

Volume: 117, Issue: 20, Pages: 10762 - 10768
Published: May 4, 2020
Abstract
Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study's replicability. Here, we trained an artificial intelligence model to estimate a paper's replicability using ground truth data on studies...
Paper Details
Title
Estimating the deep replicability of scientific findings using human and artificial intelligence
Published Date
May 4, 2020
Volume
117
Issue
20
Pages
10762 - 10768
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