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通过无偏见的无监督机器学习识别恐龙足迹的变化和分类有问题的标本
Gregor Hartmann1, Tone Blakesley2, Paige E dePolo2,3
1Department of Optics and Beamlines, Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Berlin 12489, Germany.
无监督的机器学习准确地识别了恐龙的足迹,将古代足迹分类,与专家达成80-93%的一致意见. 这种方法有助于理解鸟类和类动物的进化起源.
科学领域:
- 古生物学的古生物学
- 机器学习 机器学习
- 进化生物学 进化生物学
背景情况:
- 传统的化石分类依赖于监督的方法,这可能会引入研究人员的偏见.
- 机器学习为客观的化石识别和解决古生物学辩论提供了潜力.
研究的目的:
- 应用无监督的机器学习技术来分类恐龙的脚印.
- 客观地识别轨道制造者,并解决有关早期鸟类和类动物起源的争论.
主要方法:
- 在一个包含1974个恐龙脚印的数据库上利用了一个解的变异自编码网络.
- 确定了八个关键的形状变化特征,区分了轨道.
- 以后标记了轨迹,并使用距离指标分析了形态空间.
主要成果:
- 在无监督分析后,与专家识别达成80-93%的一致性.
- 归类的特里亚斯纪晚期 - 罗纪早期的鸟类般的轨迹与现代和化石鸟类.
- 聚合了罗纪中期的三足迹与类动物,表明更早的起源.
结论:
- 无监督机器学习为化石轨迹分析提供了强大的,不那么偏见的方法.
- 这些发现支持鸟类和类动物的更早的进化起源,而不是以前由身体化石表明的.
- DinoTracker应用程序和源代码有助于更广泛地应用这种技术.
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