Neural modeling and large language models (LLMs) has led to a significant improvement in the quality of machine translation (MT) output. While MT increasingly rivals human output, “translationese”—systematic linguistic fingerprints left by the translation process—has long been studied qualitatively, yet its quantitative boundaries remain unclear. We present an interpretable machine-learning framework that classifies Chinese-to-English human, Google-Translate, and ChatGPT outputs across news, nov