arXiv:2508.14139cs.DLcs.AI2025-08
用统计方法预测高被引论文，助力科研资源分配
The Statistical Validation of Innovation Lens
- 构建分类器分析科学发现模式
- 在多个领域准确预测高被引论文
- 适合科研管理者与资助机构参考
信息过载和科学进步的快速节奏使得评估和分配新研究提案资源变得日益困难。是否存在一种可指导决策的科学发现结构？我们通过训练分类器，在计算机科学、物理学和PubMed领域2010-2024年间成功预测了高被引研究论文，提供了此类结构的统计证据。
原文摘要 · Abstract (English)
Information overload and the rapid pace of scientific advancement make it increasingly difficult to evaluate and allocate resources to new research proposals. Is there a structure to scientific discovery that could inform such decisions? We present statistical evidence for such structure, by training a classifier that successfully predicts high-citation research papers between 2010-2024 in the Computer Science, Physics, and PubMed domains.
科研评估预测模型高被引
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