  Large language models (LLMs) have gained popularity in various fields for
their exceptional capability of generating human-like text. Their potential
misuse has raised social concerns about plagiarism in academic contexts.
However, effective artificial scientific text detection is a non-trivial task
due to several challenges, including 1) the lack of a clear understanding of
the differences between machine-generated and human-written scientific text, 2)
the poor generalization performance of existing methods caused by
out-of-distribution issues, and 3) the limited support for human-machine
collaboration with sufficient interpretability during the detection process. In
this paper, we first identify the critical distinctions between
machine-generated and human-written scientific text through a quantitative
experiment. Then, we propose a mixed-initiative workflow that combines human
experts' prior knowledge with machine intelligence, along with a visual
analytics prototype to facilitate efficient and trustworthy scientific text
detection. Finally, we demonstrate the effectiveness of our approach through
two case studies and a controlled user study with proficient researchers. We
also provide design implications for interactive artificial text detection
tools in high-stakes decision-making scenarios.
