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

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Derek McKay on behalf of the Authors (07 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (12 May 2020) by Flavia Tauro
RR by Anonymous Reviewer #1 (15 May 2020)
RR by Anonymous Reviewer #2 (26 May 2020)
ED: Publish subject to minor revisions (review by editor) (28 May 2020) by Flavia Tauro
AR by Derek McKay on behalf of the Authors (06 Jun 2020)  Author's response    Manuscript
ED: Publish as is (16 Jun 2020) by Flavia Tauro
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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.