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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מידע ביבליוגרפי

Principais autores:	Ramanakumar Sankar, Kameswara Mantha, Cooper Nesmith, Lucy Fortson, Shawn Brueshaber, Candice Hansen-Koharcheck, Glenn Orton
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Ubiquity Press 2024-12-01
סדרה:	Citizen Science: Theory and Practice
נושאים:	citizen science crowdsourcing auto-encoder semi-supervised network machine attention planetary atmospheres
גישה מקוונת:	https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/731
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!