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The paper proposes a framework that preserves individual expert interval labels by modeling them with Beta distributions and decomposing predictive uncertainty into within-, between-, and model components, using a decomposition matching technique to align them. On sea-ice concentration data, it reduces MAE by 31% over hard-label baselines and improves correlation with between-expert variation to 0.500.
The paper introduces a weakly supervised multi-label proportion learning framework for sea-ice type prediction using SAR imagery and multimodal data, achieving significant accuracy improvements over supervised baselines.