2023
DOI: 10.3390/atmos14081297
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Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum

Abstract: Panicum milliaceum is a specialty crop that maintains the economic stability of agriculture in arid and barren regions of the world. Predicting the potential geographic distribution of Panicum milliaceum globally and clarifying the ecological needs of Panicum milliaceum will help to advance the development of agriculture, which is important for the maintenance of human life and health. In this study, based on 5637 global distribution records of Panicum milliaceum, we used the MaxEnt model and ArcGIS software, … Show more

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Cited by 2 publications
(3 citation statements)
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“…Specifically, intervals for AUC values were defined as follows: (AUC � 0.60, failure), (0.60 < AUC � 0.70, poor), (0.70 < AUC � 0.80, average), (0.80 < AUC � 0.90, good), and (0.90 < AUC � 1.00, very good). Similarly, Kappa values were categorized into intervals as follows: (0.40 < Kappa � 0.55, poor), (0.55 < Kappa � 0.70, fair), (0.70 < Kappa � 0.85, good), and (0.85 < Kappa � 1.00, very good) [30][31][32].…”
Section: Plos Onementioning
confidence: 99%
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“…Specifically, intervals for AUC values were defined as follows: (AUC � 0.60, failure), (0.60 < AUC � 0.70, poor), (0.70 < AUC � 0.80, average), (0.80 < AUC � 0.90, good), and (0.90 < AUC � 1.00, very good). Similarly, Kappa values were categorized into intervals as follows: (0.40 < Kappa � 0.55, poor), (0.55 < Kappa � 0.70, fair), (0.70 < Kappa � 0.85, good), and (0.85 < Kappa � 1.00, very good) [30][31][32].…”
Section: Plos Onementioning
confidence: 99%
“…Additionally, six feature combinations (FC) were employed to optimise the model parameters: L (linear features), LQ (linear features + quadratic features), H (hinge features), LQH (linear features + quadratic features + hinge features), LQHP (linear features + quadratic features + hinge features + product features), and LQHPT (linear features + quadratic features + hinge features + product features + threshold features). After careful evaluation, the optimal combination for the regularisation multiplier and feature selection was RM and LQHPT, respectively [32].…”
Section: Plos Onementioning
confidence: 99%
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