ChatGPT is becoming a new reality. In this paper, we show how to distinguish ChatGPT-generated publications from counterparts produced by scientists. Using a newly designed supervised Machine Learning algorithm, we demonstrate how to detect machine-generated publications from those produced by scientists. The algorithm was trained using 100 real publication abstracts, followed by a 10-fold calibration approach to establish a lower-upper bound range of acceptance. In the comparison with ChatGPT content, it was evident that ChatGPT contributed merely 23\% of the bigram content, which is less than 50\% of any of the other 10 calibrating folds. This analysis highlights a significant disparity in technical terms where ChatGPT fell short of matching real science. When categorizing the individual articles, the xFakeBibs algorithm accurately identified 98 out of 100 publications as fake, with 2 articles incorrectly classified as real publications. Though this work introduced an algorithmic approach that detected the ChatGPT-generated fake science with a high degree of accuracy, it remains challenging to detect all fake records. This work is indeed a step in the right direction to counter fake science and misinformation.

通过使用一种新设计的监督机器学习算法，本研究揭示了如何区分ChatGPT生成的出版物和科学家生成的对应物。与ChatGPT内容进行比较时，明显看到ChatGPT仅贡献了23％的双字词内容，这比其他10个校准折叠中的任何一个都少于50％。尽管这项工作提出了一种检测ChatGPT生成的虚假科学的算法方法，并且准确度很高，但仍然很难检测到所有虚假记录。这项工作确实是朝着对抗虚假科学和错误信息的正确方向迈出的一步。

利用真实出版文本改进ChatGPT生成的伪科学检测：引入一种监督学习网络算法xFakeBibs