https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 Determination of bioavailable arsenic threshold and validation of modeled permissible total arsenic in paddy soil using machine learning https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51944 MTC) was considered as the dependent variable; bioavailable As (BAs), total As (TAs), pH, organic carbon (OC), available phosphorus (AvP), and available iron (AvFe) were the predictor variables. LR performed better than RF and GBM in terms of accuracy, sensitivity, specificity, kappa, precision, log loss, F1score, and MCC. From the better-performing LR model, bioavailable As (BAs), TAs, AvFe, and OC were significant variables for grain As. From the partial dependence plots (PDP) and individual conditional expectation (ICE) of the LR model, 5.70 mg kg−1 was estimated to be the limit for BAs in soil.]]> Wed 28 Feb 2024 15:34:22 AEDT ]]> Varietal differences influence arsenic and lead contamination of rice grown in mining impacted agricultural fields of Zamfara State, Nigeria https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47379 Mon 16 Jan 2023 13:46:54 AEDT ]]> Arsenic in the Soil-Plant-Human Continuum in Regions of Asia: Exposure and Risk Assessment https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54822 Fri 15 Mar 2024 08:36:58 AEDT ]]>