The Natural History Museum, London, UK;

       Department of Earth Sciences, University College London, UK;  

　　Nanjing Institute of Geology & Palaeontology, CAS, China;  

　　Faculty of Life Sciences, The University of Manchester,  Manchester. UK 

　　One approach to addressing long-standing concerns associated with the taxonomic impediment and the low reproducibility of taxonomic data is through development of automated species identification systems. Two generalized approach categories are considered relevant in this context: morphometric systems based on measurements taken from 2D images or 3D scans and analyzed by some form of discriminant analysis and machine learning systems that analyze the pixel brightness values of digital images. The former category is generally familiar to many systematists, but has rarely been used for taxonomic group-identification. The latter is less familiar, but is employed increasingly in various sorts of mathematical research, information technology, and security-related contexts. Use of either category to augment the performance of human experts is highly desirable in order to (1) raise the quality of taxonomic identifications on which so many scientific results and interpretations depend, (2) stabilize species concepts, and (3) deliver high-quality taxonomic identifications to those who need them in academic, educational, industrial, agricultural, resource management/conservation, government, and cultural (museum) sectors of the economy. Comparisons between these two approaches are needed in order to establish appropriate roles for each and to identify the limitations of each for resolving taxonomic problems in all spheres of human activity.  

　　时间：2015.5.13上午10:30 

　　地点：602谈古斋   

                                                                           脊椎动物演化与人类起源重点实验室&学生会 

　　                                                                                             2015.5.6