本頁只刊出中文翻譯與中文說明；英文原文請見下方原文連結。
原文連結
論文資訊
- 類型：已發表論文
- 日期：2020
摘要
The explosive growth of scientists, scientific journals, articles and findings in recent years 1 ,2 exponentially increases the difficulty scientists face in navigating prior knowledge and collectively reasoning over it to drive future advance 3 ,4. This challenge is exacerbated by uncertainty about the reproducibility of published findings 5 –8. The availability of massive digital archives, machine reading and extraction tools on the one hand, and automated high-throughput experiments on the other, allow us to evaluate these challenges at scale and identify novel opportunities for accelerating scientific advance 9 . Here we demonstrate a 貝氏 calculus that enables the positive prediction of robust, replicable scientific claims with findings automatically extracted from published lite
※ 此為已發表論文，全文需透過期刊付費取得