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. 2020 May 26;10:8729. doi: 10.1038/s41598-020-65619-4

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Error analysis. The error is E = Automated count – ground truth. Thus negative errors are non-detected worms (false-negative) and positive errors are detected contamination (false-positive). (a) is the absolute error percentage per day of the whole population, and the automated error (blue bars) is compared to the filtered result error by post-processing (red bars). (b) shows the automated error frequency distribution per plate for all three experiments with a positive or negative sign (in individuals), while (c) is the post-processing error frequency distribution.