Machine learning research treats beating the state of the art on a benchmark as the main kind of publishable result, so negative results — methods that did not work, conditions under which a known method fails — go largely unpublished, and compute is spent repeating attempts that others have already found not to work; registering what does not work is no less important than registering what works.

Capability: Whether the measurement made the finding