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Titlebook: Secondary Pollen Presentation; Form, Function and E P. F. Yeo Book 1993 Springer-Verlag Wien 1993 Asteraceae.Asteridae.Compositae.Dilleniid

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书目名称Secondary Pollen Presentation
副标题Form, Function and E
编辑P. F. Yeo
视频video
丛书名称Plant Systematics and Evolution - Supplementa
图书封面Titlebook: Secondary Pollen Presentation; Form, Function and E P. F. Yeo Book 1993 Springer-Verlag Wien 1993 Asteraceae.Asteridae.Compositae.Dilleniid
描述Secondary pollen presentation is presentation of pollen to vectors by structures other than anthers, either passively or via a specialized protection and delivery system. The main part of the book describes secondary pollen presentation genus-by-genus in 25 families. The subject has never been extensively reviewed, although secondary pollen presentation occurs in the largest family of flowering plants, the Asteraceae (Compositae), and a large family of great economic importance, the Leguminosae. Now material from the scattered literature is brought together and supplemented with original observations. Many species are illustrated and each family is individually discussed. The last two chapters provide an overview of the whole topic. All the main functions that secondary pollen presentation may perform can be carried out in other plants without it. It is concluded from this that the evolution of secondary pollen presentation has been subject to constraint and canalization. The floral biology of most plants with secondary pollen presentation has not been adequately studied. Appendix 1 points to a wide range of topics on which research at various technical and academic levels is neede
出版日期Book 1993
关键词Asteraceae; Asteridae; Compositae; Dilleniidae; Dipsacales; Flora; Gentianales; Liliaceae; Liliales; Malvales
版次1
doihttps://doi.org/10.1007/978-3-7091-6670-3
isbn_softcover978-3-7091-7375-6
isbn_ebook978-3-7091-6670-3Series ISSN 0172-6668
issn_series 0172-6668
copyrightSpringer-Verlag Wien 1993
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978-3-7091-7375-6Springer-Verlag Wien 1993
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P. F. Yeon and having a good-exploration strategy. Standard approaches such as variational auto-encoder (VAE) could learn such representation. However, these approaches are only designed to encode the input observations into a pre-defined latent distribution and do not take into account the dynamics of the e
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