This study considers the extent to which users with the same query agree as to what is relevant, and how what is considered relevant may translate into a retrieval algorithm and results display. To combine user perceptions of relevance with algorithm rank and to present results, we created a prototype digital library of scholarly literature. We confine studies to one population of scientists (paleontologists), one domain of scholarly scientific articles (paleo-related), and a prototype system (PaleoLit) that we built for the purpose. Based on the principle that users do not pre-suppose answers to a given query but that they will recognize what they want when they see it, our system uses a rules-based algorithm to cluster results into fuzzy categories with three relevance levels. Our system matches at least 1/3 of our participants' relevancy ratings 87% of the time. Our subsequent usability study found that participants trusted our uncertainty labels but did not value our color-coded horizontal results layout above a standard retrieval list. We posit that users make such judgments in limited time, and that time optimization per task might help explain some of our findings.