Before revision: Adopting a hypothesis-testing approach from statistics, in which, in this case, the null hypothesis is that a given item is irrelevant ( i.e., not a dog), absence of type I and type II errors ( i.e., perfect specificity and sensitivity of 100%DIF < each) corresponds respectively to perfect precision (no false positive) and perfect recall (no false negative).
After revision: Adopting a hypothesis-testing approach , where in this case, the null hypothesis is that a given item is irrelevant ( not a dog), absence of type I and type II errors ( perfect specificity and sensitivity