Objective analytical identification methods are still a minority in the praxis of paleobiological sciences. Subjective interpretation of fossils and their modifications remains a nonreplicable expert endeavor. Identification of African bovids is a crucial element in the reconstruction of paleo-landscapes, ungulate paleoecology, and, eventually, hominin adaptation and ecosystemic reconstruction. Recent analytical efforts drawing on Fourier functional analysis and discrimination methods applied to occlusal surfaces of teeth have provided a highly accurate framework to correctly classify African bovid tribes and taxa. Artificial intelligence tools, like computer vision, have also shown their potential to be objectively more accurate in the identification of taphonomic agency than human experts. For this reason, here we implement some of the most successful computer vision methods, using transfer learning and ensemble analysis, to classify bidimensional images of African bovid teeth and show that 92% of the large testing set of images of African bovid tribes analyzed could be correctly classified. This brings an objective tool to paleoecological interpretation, where bovid identification and paleoecological interpretation can be more confidently carried out.

中文翻译：

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使用迁移学习和计算机视觉对非洲牛科动物部落进行分类


客观分析鉴定方法在古生物学实践中仍然占少数。对化石及其修饰的主观解释仍然是一项不可复制的专家努力。非洲牛科动物的鉴定是重建古景观、有蹄类古生态学以及最终古人类适应和生态系统重建的关键因素。最近的分析工作利用傅立叶功能分析和应用于牙齿咬合表面的辨别方法，为正确分类非洲牛科动物部落和类群提供了一个高度准确的框架。计算机视觉等人工智能工具也显示出其在识别埋藏机构方面比人类专家更客观准确的潜力。出于这个原因，我们在这里实施了一些最成功的计算机视觉方法，使用迁移学习和集成分析，对非洲牛科动物牙齿的二维图像进行分类，并表明所分析的非洲牛科动物部落的大型测试图像集中的 92% 可以被分类。正确分类。这为古生态解释提供了客观的工具，可以更加自信地进行牛科动物鉴定和古生态解释。