Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra
نویسندگان
چکیده
منابع مشابه
Calibration of Machine Learning Models
The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the “total” or the “average” error of the model, it is also important to know how this error is distributed and how well confidence or probability estimations are mad...
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Clay fractions in soils from a transect of the Mazama ash deposit (6600-yr-old) contained more than 80% amorphous material. Instrumental neutron activation analysis was used to compare the trace element composition of the soil clay with the unweathered volcanic glass. The clay fractions had only 10% as much Na as the volcanic glass. Conversely, the rare earth element concentrations were about t...
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introduction high concentrations of metals are usually encountered in surface soil and vegetation in areas affected by mining activity (liu et al., 2006). different distribution of elements in chemical fractions result in different bioavailability; therefore knowledge of the total content of an element in soil is not a sufficient criterion to estimate the environmental implications of trace met...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2019
ISSN: 2045-2322
DOI: 10.1038/s41598-019-47751-y