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. (2021). Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Classical And Quantum Gravity, 38(19). https://doi.org/10.1088/1361-6382/ac1ccb
. (2021). Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Classical And Quantum Gravity, 38(19). https://doi.org/10.1088/1361-6382/ac1ccb
. (2020). Knowledge Tracing to Model Learning in Online Citizen Science Projects. Ieee Transactions On Learning Technologies, 13, 123-134. https://doi.org/10.1109/TLT.2019.2936480
transaction paper final figures in text.pdf (1.39 MB). (2020). Shifting forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings Of The Acm On Human-Computer Interaction, (CSCW), 36. https://doi.org/10.1145/3392841
3392841.pdf (1.13 MB). (2020). Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training. Computers In Human Behavior, 105, 106198. https://doi.org/10.1016/j.chb.2019.106198
MLGT-preprint.pdf (2.43 MB). (2019). Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning. Physical Review D, 99(8), 082002. https://doi.org/10.1103/PhysRevD.99.082002
. (2017). Gravity Spy: Humans, machines and the future of citizen science. In ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2017). https://doi.org/10.1145/3022198.3026329
cpa137-crowstonA.pdf (1.26 MB). (2017). Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science. Classical And Quantum Gravity, 34, 064003. https://doi.org/10.1088/1361-6382/aa5cea