Techniques for training a classifier to detect executional artifacts in microwell plates

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to train a classifier to detect executional artifacts in experiments involving microwell plates, by performing the steps of:

• determining one or more spatial patterns based on a first measurement value array associated with a first microwell plate;
• computing a first set of features based on the one or more spatial patterns;
• and
• executing one or more machine learning operations based on the first set of features to generate a trained classifier, wherein the trained classifier classifies sets of features associated with different microwell plates with respect to a plurality of labels that is associated with a plurality of executional artifacts.

A system comprising:

• one or more memories storing instructions;
• and
• one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
• computing first spatial information based on a measurement value array associated with a first microwell plate;
• computing a first set of features associated with low frequency spatial patterns based on the first spatial information;
• and
• executing one or more machine learning operations based on the first set of features to generate a trained machine learning model, wherein the trained machine learning model classifies sets of features associated with different microwell plates with respect to a plurality of labels that is associated with a plurality of executional artifacts.