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原文連結
論文資訊
- 類型：已發表論文
- 日期：2022-04-28
摘要
The growth of published science in recent years has escalated the difficulty that human and algorithmic 智能體s face in reasoning over prior knowledge to select the next experiment. This challenge is increased by uncertainty about the reproducibility of published findings. The availability of massive digital archives, machine reading, extraction tools and automated high-throughput experiments allows us to evaluate these challenges 計算ly at scale and identify novel opportunities to craft policies that accelerate scientific progress. Here we demonstrate a 貝氏 calculus that enables positive prediction of robust scientific claims with findings extracted from published literature, weighted by scientific, 社會 and institutional factors demonstrated to increase replicability. Illustrated with the case o
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