We present our submission to SciHal Subtask 1: coarse-grained hallucination detection for scientific question answering. We frame hallucination detection as an NLI-style three-way classification (entailment, contradiction, unverifiable) and show that simple fine-tuning of NLI-adapted encoder models on task data outperforms more elaborate feature-based pipelines and large language model prompting. In particular, DeBERTa-V3-large, a model pretrained on five diverse NLI corpora, achieves the highest weighted F1 on the public leaderboard. We additionally explore a pipeline combining joint claim–reference embeddings and NLI softmax probabilities fed into a classifier, but find its performance consistently below direct encoder fine-tuning. Our findings demonstrate that, for reference-grounded hallucination detection, targeted encoder fine-tuning remains the most accurate and efficient approach.