Geoscientific Instrumentation, Methods and Data Systems
Geoscientific Instrumentation, Methods and Data Systems
GI
Articles | Volume 9, issue 2
Articles | Volume 9, issue 2
https://doi.org/10.5194/gi-9-267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-9-267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 2
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: 3,770 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,681 873 216 3,770 354 281 283
  • HTML: 2,681
  • PDF: 873
  • XML: 216
  • Total: 3,770
  • Supplement: 354
  • BibTeX: 281
  • EndNote: 283
Views and downloads (calculated since 28 Jan 2020)
Cumulative views and downloads (calculated since 28 Jan 2020)

Viewed (geographical distribution)

Total article views: 3,770 (including HTML, PDF, and XML) Thereof 3,393 with geography defined and 377 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 31 Oct 2026
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.
Read more
Share