  Quality control is an ongoing concern in citizen science that is often
managed by replication to consensus in online tasks such as image
classification. Numerous factors can lead to disagreement, including image
quality problems, interface specifics, and the complexity of the content
itself. We conducted trace ethnography with statistical and qualitative
analyses of six Snapshot Safari projects to understand the content
characteristics that can lead to uncertainty and low consensus. This study
contributes content categorization based on aggregate classifications to
characterize image complexity, with analysis that confirms that the categories
impact classification efficiency, and an inductively generated set of
additional image quality issues that also impact volunteers' ability to
confidently classify content. The results suggest that different
conceptualizations and measures of consensus may be needed for different types
of content, and aggregate responses offer a way to identify content that needs
different handling when complexity cannot be determined $a$ $priori$.
