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A third of tested configurations in depression detection collapsed to a single-class prediction, underscoring the hidden pitfalls of current aggregation methods.
Identity leakage previously inflated Mandarin depression detection scores to 0.954, but CLeaD reveals the true performance is significantly lower, exposing critical flaws in existing methodologies.
