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Quality control is an ongoing concern in citizen science that is oftenmanaged by replication to consensus in online tasks such as imageclassification. Numerous factors can lead to disagreement, including imagequality problems, interface specifics, and the complexity of the contentitself. We conducted trace ethnography with statistical and qualitativeanalyses of six Snapshot Safari projects to understand the contentcharacteristics that can lead to uncertainty and low consensus. This studycontributes content categorization based on aggregate classifications tocharacterize image complexity, with analysis that confirms that the categoriesimpact classification efficiency, and an inductively generated set ofadditional image quality issues that also impact volunteers' ability toconfidently classify content. The results suggest that differentconceptualizations and measures of consensus may be needed for different typesof content, and aggregate responses offer a way to identify content that needsdifferent handling when complexity cannot be determined $a$ $priori$.

Is that a Duiker or Dik Dik Next to the Giraffe? Impacts of Uncertainty on Classification Efficiency in Citizen Science