The observed differences in the consistency of machine-generated text across domains can be leveraged to develop more robust and generalizable detection methods by implementing domain-specific fine-tuning strategies. By training the detection model on a diverse set of texts from various domains, the model can learn to adapt to the specific characteristics of each domain. Additionally, ensemble learning techniques can be employed to combine the strengths of multiple models trained on different domains, improving the overall detection performance. Leveraging domain-specific data augmentation techniques and transfer learning approaches can also enhance the model's ability to generalize across diverse domains.