bioRxiv / medRxiv
open access
A learned fungal ITS embedding does not outperform correctly configured alignment in open-world evaluation
1. Learned sequence embeddings are increasingly proposed for fungal ITS classification and novelty detection, but are usually evaluated against weaker versions of themselves or default-configured alignment, by AUROC alone, and with queries treated as independent. We asked whether a purpose-trained e
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