气候变化领域集成服务门户(CCPortal): Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks

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DOI	10.1016/j.palaeo.2019.03.007
	Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks
	Courtenay L.A.; Yravedra J.; Huguet R.; Aramendi J.; Maté-González M.Á.; González-Aguilera D.; Arriaza M.C.
发表日期	2019
ISSN	0031-0182
起始页码	28
结束页码	39
卷号	522
英文摘要	Since the 1980s an intense scientific debate has revolved around the hunting capacities of early hominin populations and the behavioral patterns of carnivores sharing the same ecosystem, and thus competing for the same resources. This debate, commonly known as the hunter-scavenger debate, fostered the emergence of a new research line into the Bone Surface Modifications (BSMs) produced by both taphonomic agents. Throughout the following 20 years, multiple studies concerning the action of carnivores have been developed, with a particular focus on the oldest archaeological sites in East Africa. Recent technological advances applied to taphonomy have provided new insight into carnivore BSMs. A newly developed part of this work relies on Geometric Morphometrics (GMM) studies aimed at discerning carnivore agency through the morphologic characterization of tooth scores and pits. GMM studies have produced promising results, however methodological limitations are still present. This paper presents the first combined application of Machine Learning (ML) algorithms and GMM to the analysis of carnivore tooth marks, generating classification rates of 100% between carnivore species in some cases. © 2019 Elsevier B.V.
英文关键词	Artificial Intelligence; Bone Surface modifications; Statistics; Taphonomy
语种	英语
scopus关键词	algorithm; archaeology; artificial intelligence; bone; carnivore; hominid; machine learning; morphology; morphometry; statistical analysis; taphonomy; tooth; East Africa
来源期刊	Palaeogeography, Palaeoclimatology, Palaeoecology
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/150999
作者单位	Àrea de Prehistòria, Universitat Rovira i Virgili (URV), Avinguda de Catalunya 35, Tarragona, 43002, Spain; Institut de Paleoecologia Humana i Evolució Social (IPHES), Zona Educacional, Campus Sescelades URV (Edifici W3) E3, Tarragona, 43700, Spain; Department of Prehistory, Complutense University, Prof. Aranguren s/n, Madrid, 28040, Spain; C. A. I. Archaeometry and Archaeological Analysis, Complutense University, Professor Aranguren s/n, Madrid, 28040, Spain; Unit Associated to CSIC, Departamento de Paleobiologia, Museo de Ciencias Naturales, calle José Gutiérrez Abascal, s/n, Madrid, 28006, Spain; IDEA (Institute of Evolution in Africa), Covarrubias 36, Madrid, 28010, Spain; Department of Cartography and Terrain Engineering, Polytechnic School of Avila, University of Salamanca, Hornos Caleros 50, Avila, 05003, Spain; School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag 32050, South Africa; Centre of Excellence in Paleosciences, University of the Witwat...
推荐引用方式 GB/T 7714	Courtenay L.A.,Yravedra J.,Huguet R.,et al. Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks[J],2019,522.
APA	Courtenay L.A..,Yravedra J..,Huguet R..,Aramendi J..,Maté-González M.Á..,...&Arriaza M.C..(2019).Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks.Palaeogeography, Palaeoclimatology, Palaeoecology,522.
MLA	Courtenay L.A.,et al."Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks".Palaeogeography, Palaeoclimatology, Palaeoecology 522(2019).