A researcher's Q denotes their ability in scientific research as a real number. Due to their short presence in the academic environment, junior researchers have unstable Q values. This article aims to present a model that uses data from junior researchers’ first years of publication to predict their stable Q values. We tested the deep model and the linear regression model and compared their accuracies. We have obtained reliable results showing that the predicted values estimated with both models are better than the estimated Q values computed with the Q model itself when using only data from the first five years of publication. Lastly, we note that both approaches are robust approaches to deal with the inflation of citation bias.

研究者的Q表示他们在科学研究中的能力，是一个真实的数字。由于他们在学术环境中的存在时间短，初级研究人员的Q值不稳定。本文旨在提出一个模型，该模型使用来自初级研究人员发表后第一年的数据来预测其稳定的Q值。我们测试了深度模型和线性回归模型，并比较了它们的准确性。我们已经获得了可靠的结果，表明当仅使用发布前五年的数据时，使用两种模型估算的预测值要好于使用Q模型本身计算的估算Q值。最后，我们注意到这两种方法都是应对引用偏差膨胀的有效方法。