Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
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- 作者：Shu-Feng Lia ; e ; Fré ; dé ; ric M.B. Jacquesa ; Robert A. Spicerc ; Tao Sua ; Teresa E.V. Spicerd ; Jian Yangd ; Zhe-Kun Zhoua ; b ; zhouzk@xtbg.ac.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Artificial neural networks ; Climate ; CLAMP ; CLANN ; Fossil ; Leaf physiognomy
- 刊名：Palaeogeography, Palaeoclimatology, Palaeoecology
- 出版年：2016
- 出版时间：15 January 2016
- 年：2016
- 卷：442
- 期：Complete
- 页码：1-11
- 全文大小：614 K
文摘
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We developed an artificial neural network to test leaf form/climate relationships.
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The new algorithm (CLANN) reveals a high-resolution climatic signal in leaf form.
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CLANN predictions are repeatable, robust to information loss, and precise.
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The new method is applicable to fossil leaf data and could form a new climate proxy.