Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA
Yulong Zhang
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
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Total article views: 3,082 (including HTML, PDF, and XML)
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2,235
725
122
3,082
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HTML: 2,235
PDF: 725
XML: 122
Total: 3,082
Supplement: 284
BibTeX: 131
EndNote: 198
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Total article views: 1,866 (including HTML, PDF, and XML)
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EndNote
1,456
338
72
1,866
130
101
171
HTML: 1,456
PDF: 338
XML: 72
Total: 1,866
Supplement: 130
BibTeX: 101
EndNote: 171
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Total article views: 1,216 (including HTML, PDF, and XML)
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779
387
50
1,216
154
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27
HTML: 779
PDF: 387
XML: 50
Total: 1,216
Supplement: 154
BibTeX: 30
EndNote: 27
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Viewed (geographical distribution)
Total article views: 3,082 (including HTML, PDF, and XML)
Thereof 2,965 with geography defined
and 117 with unknown origin.
Total article views: 1,866 (including HTML, PDF, and XML)
Thereof 1,783 with geography defined
and 83 with unknown origin.
Total article views: 1,216 (including HTML, PDF, and XML)
Thereof 1,182 with geography defined
and 34 with unknown origin.
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of...