Articles | Volume 11, issue 1
https://doi.org/10.5194/amt-11-291-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Download
- Final revised paper (published on 15 Jan 2018)
- Supplement to the final revised paper
- Preprint (discussion started on 09 Aug 2017)
- Supplement to the preprint
Interactive discussion
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
-
RC1: 'Review of "Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance" by Zimmerman et al.', Anonymous Referee #1, 11 Sep 2017
- AC1: 'Response to Reviewer #1', Naomi Zimmerman, 02 Nov 2017
-
RC2: 'referee's comments', Anonymous Referee #2, 17 Sep 2017
- AC2: 'Response to Reviewer #2', Naomi Zimmerman, 02 Nov 2017
-
SC1: 'Review of Zimmerman et al.', Eben Cross, 04 Oct 2017
- AC3: 'Response to Comments from Aerodyne Research Inc.', Naomi Zimmerman, 02 Nov 2017
Peer-review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Naomi Zimmerman on behalf of the Authors (03 Nov 2017)
Author's response
Manuscript
ED: Publish subject to technical corrections (20 Nov 2017) by Dominik Brunner
AR by R Subramanian on behalf of the Authors (28 Nov 2017)
Manuscript