The challenge of predicting confidence intervals and distributions can be faced with several statistical techniques. The chosen method for my approach was a direct inference of a weighted combination of multivariate normal distributions along the 6 dimensions of the target variables, to provide information about mutual correlations, using a custom version of the Mixture Density Network, on top of a series of convolution layers for the spectra, and a dense layer for some engineered features from the auxiliary data, against an adapted version of the dataset.