Understanding Confusion: A Case Study of Training a Machine Model to Predict and Interpret Consensus From Volunteer Labels
Citizen science has become a valuable and reliable method for interpreting and processing big datasets, and is vital in the era of ever-growing data volumes. However, there are inherent difficulties in the generating labels from citizen scientists, due to the inherent variability between the members...
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Main Authors: | Ramanakumar Sankar, Kameswara Mantha, Cooper Nesmith, Lucy Fortson, Shawn Brueshaber, Candice Hansen-Koharcheck, Glenn Orton |
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Format: | Article |
Language: | English |
Published: |
Ubiquity Press
2024-12-01
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Series: | Citizen Science: Theory and Practice |
Subjects: | |
Online Access: | https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/731 |
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