We present SAINE, an Scientific Annotation and Inference ENgine based on a set of standard open-source software, such as Label Studio and MLflow. We show that our annotation engine can benefit the further development of a more accurate classification. Based on our previous work on hierarchical discipline classifications, we demonstrate its application using SAINE in understanding the space for scholarly publications. The user study of our annotation results shows that user input collected with the help of our system can help us better understand the classification process. We believe that our work will help to foster greater transparency and better understand scientific research. Our annotation and inference engine can further support the downstream meta-science projects. We welcome collaboration and feedback from the scientific community on these projects. The demonstration video can be accessed from https://youtu.be/yToO-G9YQK4. A live demo website is available at https://app.heartex.com/user/signup/?token=e2435a2f97449fa1 upon free registration.

翻译：我们提出SAINE——基于Label Studio和MLflow等标准开源软件构建的科学研究标注与推理引擎。研究表明，该标注引擎能够促进更精确分类体系的进一步发展。结合团队前期在学科分层分类方面的工作，我们展示了SAINE在理解学术出版物空间中的应用效果。标注结果的用户研究表明，借助本系统收集的用户输入有助于我们更深入地理解分类过程。我们相信，本项工作将推动学术研究透明度的提升并加深对科学研究的理解。该标注与推理引擎可进一步支持下游元科学研究项目。我们热忱欢迎科学界就这些项目展开合作并提供反馈。演示视频可通过https://youtu.be/yToO-G9YQK4 访问；体验网站经免费注册后可通过https://app.heartex.com/user/signup/?token=e2435a2f97449fa1 登录使用。