Evaluation of Observationally Based Models through Salience and Salience Maps.

Observational scientists use data—either qualitative or quantitative—to construct conceptual models but do not indicate which data are particularly important for the building of that model. We propose the use of salience to denote the relevance of data in supporting a particular model. Salience is effectively a weighting factor for observational data with respect to a model. We propose a scale to characterize the salience of data (from low to high): no attribution, negligible, peripheral, pertinent, important, and paramount. Data that are inconsistent with a given model are categorized with negative salience values. For fields in which the spatial distribution of salience can be visually displayed, we introduce the concept of salience maps. We provide an example of the use of salience rankings and construction of a salience map for the Sage Hen Flat pluton in the White Mountains in eastern California. The use of salience and salience maps is a way to provide increased reliability and trustworthiness of models, facilitate communication, promote inclusiveness, and allow for scientists to more effectively build off prior data in the observational sciences. [ABSTRACT FROM AUTHOR]