Taphonomists have long struggled with identifying carnivore agency in bone accumulation and modification. Now that several taphonomic techniques allow identifying carnivore modification of bones, a next step involves determining carnivore type. This is of utmost importance to determine which carnivores were preying on and competing with hominins and what types of interaction existed among them during prehistory. Computer vision techniques using deep architectures of convolutional neural networks (CNN) have enabled significantly higher resolution in the identification of bone surface modifications (BSM) than previous methods. Here, we apply these techniques to test the hypothesis that different carnivores create specific BSM that can enable their identification. To make differentiation more challenging, we selected two types of carnivores (lions and jaguars) that belong to the same mammal family and have similar dental morphology. We hypothesize that if two similar carnivores can be identified by the BSM they imprint on bones, then two more distinctive carnivores (e.g. hyenids and felids) should be more easily distinguished. The CNN method used here shows that tooth scores from both types of felids can be successfully classified with an accuracy greater than 82%. The first hypothesis was successfully tested. The next step will be to differentiate diverse carnivore types involving a wider range of carnivore-made BSM. The present study demonstrates that resolution increases when combining two different disciplines (taphonomy and artificial intelligence computing) in order to test new hypotheses that could not be addressed with traditional taphonomic methods.

中文翻译：

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深度学习提高了穴位学分辨率：区分狮子和美洲虎牙印的准确性高

Taphonomists 长期以来一直在努力识别食肉动物在骨骼积累和修饰中的作用。现在有几种埋藏技术可以识别食肉动物的骨骼修饰，下一步涉及确定食肉动物的类型。这对于确定哪些食肉动物正在捕食人类并与之竞争以及史前时期它们之间存在哪些类型的相互作用至关重要。使用卷积神经网络 (CNN) 深层架构的计算机视觉技术在识别骨表面修饰 (BSM) 方面比以前的方法具有更高的分辨率。在这里，我们应用这些技术来测试不同的食肉动物会产生特定的 BSM 来识别它们的假设。为了使差异化更具挑战性，我们选择了属于同一哺乳动物家族并具有相似牙齿形态的两种食肉动物（狮子和美洲虎）。我们假设，如果两个相似的食肉动物可以通过它们印在骨头上的 BSM 来识别，那么两个更独特的食肉动物（例如鬣狗和猫科动物）应该更容易区分。这里使用的 CNN 方法表明，两种猫科动物的牙齿评分都可以成功分类，准确率超过 82%。第一个假设被成功验证。下一步将是区分不同的食肉动物类型，包括更广泛的食肉动物制造的 BSM。