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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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GI | Articles | Volume 9, issue 2
Geosci. Instrum. Method. Data Syst., 9, 267–273, 2020
https://doi.org/10.5194/gi-9-267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Instrum. Method. Data Syst., 9, 267–273, 2020
https://doi.org/10.5194/gi-9-267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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
Publications Copernicus
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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.
Researchers are making increasing use of machine learning to improve accuracy, efficiency and...
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