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Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced automatic wildlife recognition. However, current methods are hampered by a dependence on large static data sets when wildlife data is intrinsically dynamic and involves long-tailed distributions. These two drawbacks can be overcome through a hybrid combination of machine learning and humans in the loop. Our proposed iterative human and automated identification approach is capable of learning from wildlife imagery data with a long-tailed distribution. Additionally, it includes self-updating learning that facilitates capturing the community dynamics of rapidly changing natural systems. Extensive experiments show that our approach can achieve a ~90% accuracy employing only ~20% of the human annotations of existing approaches. Our synergistic collaboration of humans and machines transforms deep learning from a relatively inefficient post-annotation tool to a collaborative on-going annotation tool that vastly relieves the burden of human annotation and enables efficient and constant model updates., Comment: This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. It is published in Nature Machine Intelligence: https://www.nature.com/articles/s42256-021-00393-0

This study assesses whether Tufts University's campus culture was successful at infusing civic-mindedness in all undergraduates. Civically-minded undergraduates were defined as students who were involved in civic engagement activities as well as those who held civic attitudes and values. A structural equation model was developed and findings revealed that the campus environment had a significant positive impact on civic values and beliefs and a positive indirect effect on civic engagement activities. The model confirmed that there is a supportive campus culture and provides evidence that the institution's mission is successful and verifiable. Sample of Civic Engagement Questions from Senior Survey is appended. (Contains 2 tables, 2 figures and 8 footnotes.)