As a new CryoSPARC user, I am doing my homework by reading the guide. I cannot wrap my head around the “Initial classification uncertainty factor” (ICUF)
I am struggling to formulate an argument for changing ICUF when optimizing CryoSPARC’s 2D classification. I understand how it influences alignment, but when it comes to the reason for changing it and the expected outcome, I find some of the explanations in the CS’s guide contradictory:
From the guide:
“It can be helpful to increase this parameter when good and bad particles are expected to look very similar…”,
but then:
“The end result is that 2D Classification jobs with a higher ICUF tend to have more classes which look like each other, and more classes that look like the “average” particle, rather than rare objects or junk.”
Let’s imagine a situation in which I say, “It might help to increase the ICUF, because <observation>.”
My question is: what would constitute that observation.