基于机器学习的选在癌症研究中的潜在造纸厂出版物:方法论和横截面研究
Baptiste Scancar1, Jennifer A Byrne2,3, David Causeur1
1IRMAR UMR 6625 CNRS, L'Institut Agro, Rennes, France.
概括
一个机器学习模型将近10%的癌症研究论文识别为潜在的欺诈性论文,这表明所有类型的期刊上的造纸厂出版物存在重大和日益严重的问题.
科学领域:
- 图书统计学 图书统计学
- 科学研究中的人工智能
- 在瘤学瘤学.
背景情况:
- 造纸厂的出版物对科学完整性构成威胁.
- 区分欺诈性论文与真正的研究对于可靠的癌症研究至关重要.
研究的目的:
- 开发和验证用于识别造纸厂出版物的机器学习模型.
- 评估癌症研究文献中潜在的欺诈性论文的流行情况.
主要方法:
- 一个基于BERT的文本分类模型被训练在收缩的造纸厂出版物上.
- 该模型用于选PubMed (1999-2024) 的260万篇癌症研究文章.
主要成果:
- 该模型在识别类似造纸厂的内容时达到91%的准确性.
- 近10% (9.87%) 的查癌症研究论文被标记,从1999年到2024年有显著的增加.
- 标志性论文集中在基础研究和特定癌症类型上,其中很大一部分与中国机构有关.
结论:
- 造纸厂的出版物在癌症研究领域中代表着一个实质性的和不断升级的问题.
- 这个问题超出了低影响期刊的范围,需要广泛的意识和协作行动.
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