Prof Chris Lintott

Abstract:

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.

Abstract:

We investigate the development of scientific content knowledge of volunteers participating in online citizen science projects in the Zooniverse (www.zooniverse.org). We use econometric methods to test how measures of project participation relate to success in a science quiz, controlling for factors known to correlate with scientific knowledge. Citizen scientists believe they are learning about both the content and processes of science through their participation. We don’t directly test the latter, but we find evidence to support the former - that more actively engaged participants perform better in a project-specific science knowledge quiz, even after controlling for their general science knowledge. We interpret this as evidence of learning of science content inspired by participation in online citizen science.

Abstract:

We present high resolution observations of a sample of 75 K2 targets from Campaigns 1-3 using speckle interferometry on the Southern Astrophysical Research (SOAR) telescope and adaptive optics (AO) imaging at the Keck II telescope. The median SOAR $I$-band and Keck $K_s$-band detection limits at 1'' were $\Delta m_{I}=4.4$ mag and $\Delta m_{K_s}=6.1$ mag, respectively. This sample includes 37 stars likely to host planets, 32 targets likely to be EBs, and 6 other targets previously labeled as likely planetary false positives. We find nine likely physically bound companion stars within 3'' of three candidate transiting exoplanet host stars and six likely eclipsing binaries (EB). Six of the nine detected companions are new discoveries, one of them associated with a planet candidate (EPIC 206061524). Among the EB candidates, companions were only found near the shortest period ones ($P<3$ days), which is in line with previous results showing high multiplicity near short-period binary stars. This high resolution data, including both the detected companions and the limits on potential unseen companions, will be useful in future planet vetting and stellar multiplicity rate studies for planets and binaries.