STAG-CN applies a spatio-temporal graph convolutional network to beehive sensor streams on a dual physical-climatic adjacency graph, achieving F1=0.607 at three-day disease onset prediction where climatic correlations alone match full-model performance.
Within-day variation in continuous hive weight data as a measure of honey bee colony activity
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STAG-CN: Spatio-Temporal Apiary Graph Convolutional Network for Disease Onset Prediction in Beehive Sensor Networks
STAG-CN applies a spatio-temporal graph convolutional network to beehive sensor streams on a dual physical-climatic adjacency graph, achieving F1=0.607 at three-day disease onset prediction where climatic correlations alone match full-model performance.