Identifying the bone-breaker at the Navalmaíllo Rock Shelter (Pinilla del Valle, Madrid) using machine learning algorithms

Moclán, Abel; Huguet, Rosa; Márquez, Belén; Laplana, César; Arsuaga, Juan Luís; Pérez‐González, Alfredo; Baquedano, Enrique

doi:10.1007/s12520-020-01017-1

Cited by 17 publications

(8 citation statements)

References 94 publications

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“…From a similar perspective, techniques in remote sensing, photogrammetry and microscopy also provide distinct advantages for the collection of different types of data, supported in many cases by the use of high resolution metric data 37,[58][59][60] . Likewise, the use of computational learning has also proven a useful diagnostic tool for the analysis of fracture plane patterns 61 , obtaining high classification rates when applied to archaeological samples as well 62 . From another perspective, computer vision applications can also be considered an interesting development in the field of taphonomy 63 .…”

Section: Discussionmentioning

confidence: 99%

Developments in data science solutions for carnivore tooth pit classification

Courtenay

Herranz‐Rodrigo

González‐Aguilera

et al. 2021

Sci Rep

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Competition for resources is a key question in the study of our early human evolution. From the first hominin groups, carnivores have played a fundamental role in the ecosystem. From this perspective, understanding the trophic pressure between hominins and carnivores can provide valuable insights into the context in which humans survived, interacted with their surroundings, and consequently evolved. While numerous techniques already exist for the detection of carnivore activity in archaeological and palaeontological sites, many of these techniques present important limitations. The present study builds on a number of advanced data science techniques to confront these issues, defining methods for the identification of the precise agents involved in carcass consumption and manipulation. For the purpose of this study, a large sample of 620 carnivore tooth pits is presented, including samples from bears, hyenas, jaguars, leopards, lions, wolves, foxes and African wild dogs. Using 3D modelling, geometric morphometrics, robust data modelling, and artificial intelligence algorithms, the present study obtains between 88 and 98% accuracy, with balanced overall evaluation metrics across all datasets. From this perspective, and when combined with other sources of taphonomic evidence, these results show that advanced data science techniques can be considered a valuable addition to the taphonomist’s toolkit for the identification of precise carnivore agents via tooth pit morphology.

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Section: Discussionmentioning

confidence: 99%

Developments in data science solutions for carnivore tooth pit classification

Courtenay

Herranz‐Rodrigo

González‐Aguilera

et al. 2021

Sci Rep

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“…These studies demonstrated that the kinds of prey and their ages (e.g., Stiner 1990;Bunn and Pickering 2010), skeletal part representation and fragmentation (e.g., Blumenschine 1986Blumenschine 1988Marean and Spencer 1991;Marean et al 1992), and the types of bone surface modifications, their locations, and frequencies (e.g., Blumenschine 1986Blumenschine 1988Capaldo 1997;Domínguez-Rodrigo 1999) provide reliable insights into the agent(s) responsible for the accumulation of fossil bone assemblages. In addition to these more traditional methods, researchers are now employing a variety of sophisticated high-resolution imaging (e.g., Pante et al 2017;Courtenay et al 2019), multivariate analyses (e.g., Domínguez-Rodrigo and Yravedra 2009;Domínguez-Rodrigo and Pickering 2010), and machine learning (e.g., Harris et al 2017;Domínguez-Rodrigo 2019;Moclán et al 2020) techniques.…”

Section: Introductionmentioning

confidence: 99%

Taphonomy of an excavated striped hyena (Hyaena hyaena) den in Arabia: implications for paleoecology and prehistory

Stewart

Andrieux

Clark-Wilson

et al. 2021

Archaeol Anthropol Sci

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Studies of modern carnivore accumulations of bone (i.e., neo-taphonomy) are crucial for interpreting fossil accumulations in the archaeological and paleontological records. Yet, studies in arid regions have been limited in both number and detailed taphonomic data, prohibiting our understanding of carnivore bone-accumulating and -modifying behavior in dry regions. Here, we present a taphonomic analysis of an impressive carnivore-accumulated bone assemblage from the Umm Jirsan lava tube in the Harrat Khaybar region, Saudi Arabia. The size and composition of the bone accumulation, as well as the presence of hyena skeletal remains and coprolites, suggest that the assemblage was primarily accumulated by striped hyena (Hyaena hyaena). Our findings (1) identify potentially useful criteria for distinguishing between accumulations generated by different species of hyenas; (2) emphasize the need for neo-taphonomic studies for capturing the full variation in carnivore bone-accumulating and modifying behavior; (3) suggest that under the right settings, striped hyena accumulations can serve as good proxies for (paleo)ecology and livestock practices; and (4) highlight the potential for future research at Umm Jirsan, as well as at the numerous nearby lava tube systems. We encourage continued neo-taphonomic efforts in regions important in human prehistory, particularly in arid zones, which have received little research attention.

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“…The incorporation of digital imaging and data extraction opens opportunities for using powerful computational tools such as machine learning which has been employed in bone modification studies (Arriaza, Aramendi, Maté-González, Yravedra, & Stratford, 2021;Byeon et al, 2019;Cifuentes-Alcobendas & Domínguez-Rodrigo, 2019;Courtenay et al, 2020;Domínguez-Rodrigo, 2019;Domínguez-Rodrigo & Baquedano, 2018;Domínguez-Rodrigo, Fernández-Jaúregui, Cifuentes-Alcobendas, & Baquedano, 2021;Domínguez-Rodrigo, Wonmin, et al, 2017;Jiménez-García, Abellán, Baquedano, Cifuentes-Alcobendas, & Domínguez-Rodrigo, 2020;Jiménez-García, Aznarte, Abellán, Baquedano, & Domínguez-Rodrigo, 2020;Moclán, Domínguez-Rodrigo, & Yravedra, 2019;Moclán et al, 2020;Pizarro-Monzo & Domínguez-Rodrigo, 2020). However, most of these papers have focused on the use of machine learning for discriminating bone surface modifications.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent

Yezzi-Woodley¹,

Terwilliger²,

Li³

et al. 2022

Preprint

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Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation of large animal resources and the effects those subsistence behaviors had on early hominin evolution. However, current methods, particularly in the area of fracture pattern analysis as a signal of marrow exploitation, have failed to overcome equifinality. Furthermore, researchers debate the replicability and validity of current and emerging methods for analyzing bone modifications. Here we present a new approach to fracture pattern analysis aimed at distinguishing bone fragments resulting from hominin bone breakage and those produced by carnivores. This new method uses 3D models of fragmentary bone to extract a much richer dataset that is more transparent and replicable than feature sets previously used in fracture pattern analysis. Supervised machine learning algorithms are properly used to classify bone fragments according to agent of breakage with average mean accuracy of 77% across tests.

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Identifying the bone-breaker at the Navalmaíllo Rock Shelter (Pinilla del Valle, Madrid) using machine learning algorithms

Cited by 17 publications

References 94 publications

Developments in data science solutions for carnivore tooth pit classification

Developments in data science solutions for carnivore tooth pit classification

Taphonomy of an excavated striped hyena (Hyaena hyaena) den in Arabia: implications for paleoecology and prehistory

Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent

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