Articles | Volume 9, issue 2
https://doi.org/10.5194/gi-9-267-2020
https://doi.org/10.5194/gi-9-267-2020
Research article
 | 
09 Jul 2020
Research article |  | 09 Jul 2020

Auroral classification ergonomics and the implications for machine learning

Derek McKay and Andreas Kvammen

Viewed

Total article views: 2,264 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,622 560 82 2,264 164 98 87
  • HTML: 1,622
  • PDF: 560
  • XML: 82
  • Total: 2,264
  • Supplement: 164
  • BibTeX: 98
  • EndNote: 87
Views and downloads (calculated since 28 Jan 2020)
Cumulative views and downloads (calculated since 28 Jan 2020)

Viewed (geographical distribution)

Total article views: 2,264 (including HTML, PDF, and XML) Thereof 1,930 with geography defined and 334 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Jan 2025
Download
Short summary
Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data because of the conditions (or ergonomics) of the human trainers. As using machine-learning techniques in auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.