An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Scaled conformal prediction using aleatoric uncertainty estimates and class-wise calibration produces sharper valid prediction intervals for object detection than unscaled variants, with up to 19% higher IoU and 39% lower interval scores on driving datasets.
citing papers explorer
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Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI
An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
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Probabilistic Object Detection with Conformal Prediction
Scaled conformal prediction using aleatoric uncertainty estimates and class-wise calibration produces sharper valid prediction intervals for object detection than unscaled variants, with up to 19% higher IoU and 39% lower interval scores on driving datasets.