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CatBoost
Machine Learning Identifies Optimal Biochar Pyrolysis Temperatures and Physical Benchmarks to Maximize Soil Cation Exchange Capacity by Over Ninety-Six Percent
CatBoost AI Model Hits 98.8% (R2) Accuracy in Predicting Water-Cleaning Biochar Performance
Optimized Biochar from Tea Waste Achieves 82.66% Pollutant Removal, With RNN Model Predicting Performance at an R2 of 0.960
Harnessing Machine Learning to Predict CO2 Adsorption in Biochar: SVR and CatBoost Models Achieve 93% Accuracy
Predicting Ammonia Removal with Biochar: A Machine Learning Approach
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