Watson's final answer merging and ranking component considers a pre-defined set of features and applies a machine learned model to score each candidate answer.
since each candidate has multiple, and generally a varying number of supporting passages, we use a merger to combine passage scores for < candidate answer, passage > pairs into a fixed set of features.