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: 3,222 (including HTML, PDF, and XML)
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EndNote
2,328
767
127
3,222
318
139
208
HTML: 2,328
PDF: 767
XML: 127
Total: 3,222
Supplement: 318
BibTeX: 139
EndNote: 208
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Total article views: 1,986 (including HTML, PDF, and XML)
HTML
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Total
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EndNote
1,535
374
77
1,986
145
106
180
HTML: 1,535
PDF: 374
XML: 77
Total: 1,986
Supplement: 145
BibTeX: 106
EndNote: 180
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Total article views: 1,236 (including HTML, PDF, and XML)
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Total
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793
393
50
1,236
173
33
28
HTML: 793
PDF: 393
XML: 50
Total: 1,236
Supplement: 173
BibTeX: 33
EndNote: 28
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Viewed (geographical distribution)
Total article views: 3,222 (including HTML, PDF, and XML)
Thereof 3,108 with geography defined
and 114 with unknown origin.
Total article views: 1,986 (including HTML, PDF, and XML)
Thereof 1,905 with geography defined
and 81 with unknown origin.
Total article views: 1,236 (including HTML, PDF, and XML)
Thereof 1,203 with geography defined
and 33 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...