In @tremblap’s original example, it was using something like pitch confidence to determine what the actual matching criteria for a given source/target was. So if the pitch confidence was high, you would then weigh the pitch value highly and carry on, whereas if the pitch confidence was low, you can ignore pitch altogether in your query.
Given the overall fluid context, this requires a “fork” of each dataset with and without pitch (and/or other variations/massagings). This works well for this kind of binary forking, but beyond that, it gets a little bit more problematic.