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Abstract:

© 2016. The American Astronomical Society. All rights reserved. We present a statistical study of the distribution and physical properties of cold, dense material in and around the inner Galactic Plane near-infrared bubbles as cataloged by the Milky Way Project citizen scientists. Using data from the Atacama Pathfinder Experiment (APEX) Telescope Large Area Survey of the Galaxy 870 μm survey, we show that 48 ± 2% of all cold clumps in the studied survey region (|l| ≤ 65°, |b| ≤ 1°) are found in close proximity to a bubble, and 25 ± 2% appear directly projected toward a bubble rim. A two-point correlation analysis confirms the strong correlation of massive cold clumps with expanding bubbles. It shows an overdensity of clumps along bubble rims that grows with increasing bubble size, which shows how interstellar medium material is reordered on large scales by bubble expansion around regions of massive star formation. The highest column density clumps appear to be resistent to the expansion, remaining overdense toward the bubbles' interior rather than being swept up by the expanding edge. Spectroscopic observations in ammonia show that cold dust clumps near bubbles appear to be denser, hotter, and more turbulent than those in the field, offering circumstantial evidence that bubble-associated clumps are more likely to be forming stars. These observed differences in physical conditions persist beyond the region of the bubble rims.

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 91̽��ing the aggregated answer (fraction 91̽��), 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.