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The paper presents the largest controlled scaling study for Earth-observation foundation models, showing that pretraining loss poorly predicts downstream performance and providing an optimal compute allocation rule. It trains scaled pixel-wise models (0.5B and 1B parameters) and distills them into compact student models that outperform larger open and proprietary models.