We describe our submission to SemEval-2026 Task 13, addressing binary detection (Subtask A), generator attribution (Subtask B), and hybrid/adversarial authorship classification (Subtask C) of machine-generated code (MGC). For Subtask A, we fine-tune two CodeBERT models with complementary sampling strategies and apply percentile-based post-hoc calibration, improving Macro-F1 from 0.47 to 0.56 without additional training. For Subtask B, we combine TF-IDF n-grams, frozen CodeBERT embeddings, and language features with XGBoost, using synthetic augmentation and class weighting to handle an 11-class dataset skewed 88% toward the human class, achieving Macro-F1 of 0.289. For Subtask C, we fine-tune a CodeBERT classifier for four-way authorship classification, achieving Macro-F1 of 0.49. Our results highlight the importance of probability calibration for binary detection and class balancing for multi-class attribution.