An interpretable XGBoost-SHAP analysis in Hebei Province, China, shows that large, continuous blue-green patches dominate carbon sequestration, with distinct thresholds ...
A study evaluating South American precipitation records from 1982 to 2020 finds that while combined climate modes yield substantial regional ...
A machine-learning framework combining neural-network prediction, SHAP explainability and genetic-algorithm optimization screened tensile strength in a 10,000-record fiber-reinforced polymer composite ...
A new explainable AI framework integrates bioelectrical impedance and clinical data to screen for cancer in peritoneal dialysis patients, showing ...
Researchers applied explainable machine learning models to identify key factors predicting pipe failures across an urban water distribution network in ...
An XGBoost machine learning analysis of the Nansi Lake Basin revealed key community-scale carbon emission drivers and simulated potential reductions ...
A multi-tissue metabolomic analysis reveals that five distinct lifespan-extending interventions induce shared metabolic reprogramming in male mice well before survival ...
A matched satellite analysis of California's 2020 Creek Fire found increased snow persistence only in the most severely burned areas, ...
A cross-sectional survey of 2,148 residents in Tsukuba City, Japan, found that each additional nocturnal void was independently associated with ...
Researchers developed an interpretable XGBoost-SHAP framework to prioritize noncoding variants and identify nonlinear regulatory interactions affecting HNF4A binding in pancreatic ...
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