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. 2020 Aug 12;10:13665. doi: 10.1038/s41598-020-70688-6

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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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The performance of our primary model. Upper row: precision-recall curve. A point on the precision-recall curve is determined by considering all pig behaviours detected with a given threshold as positive predictions, then calculating the resulting precision and recall for that threshold. Below row: miss rate against FPPI. This characteristic curve obtained with similar mechanisms of the PR curve however to represent miss detected pigs; both axes are logarithmically scaled.