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相关概念视频

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Lesson: Translation
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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使用多语言预训练模型进行环境证据合成的西班牙语文本分类.

Violeta Berdejo-Espinola1,2, Ákos Hajas3, Richard Cornford4

  • 1School of the Environment, The University of Queensland, Brisbane, Australia. v.berdejoespinola@uq.net.au.

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概括

人工智能 (AI) 现在可以选非英语研究的证据综合,克服单语言研究的局限性. 这种人工智能工具有效过不相关的论文,确保在环境研究中没有错过任何相关研究.

关键词:
生物多样性保护 生物多样性保护证据综合研究可解释的人工智能语言障碍 语言障碍 语言障碍多语言语言模型的多语言模型.自然语言处理自然语言处理.非英语的非英语语言这就是 SHAP SHAP 的意思.

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 信息科学 信息科学 信息科学

背景情况:

  • 环境科学中的证据综合通常是单语言的 (以英语为主).
  • 这种语言偏见可能导致结果偏差和错误的政策决策.
  • 人工智能 (AI) 提出了一个潜在的解决方案,用于结合非英语证据.

研究的目的:

  • 开发和评估人工智能驱动的文本分类器,用于在环境综合中选非英语证据.
  • 评估机器学习模型在识别相关的西班牙语生物多样性保护论文方面的有效性.
  • 为了减少手工劳动和潜在的偏差在证据综合选.

主要方法:

  • 利用了关于生物多样性保护的西班牙语同行评审论文.
  • 开发了用于文字分类的监督机器学习模型.
  • 采用预先训练的多语言模型来编码文本,并为不平衡的数据集使用类权重.

主要成果:

  • 性能最好的模型实现了100%的召回,没有错过任何相关研究.
  • 超过70%的无关文件仅根据标题和摘要进行过.
  • 该方法有效地处理了一个高度不平衡的数据集 (0.79%).

结论:

  • 人工智能,特别是多语言语言模型和类权重,可以创建有效的非英语语言分类器.
  • 这种方法大大减少了证据综合的文件选的时间和精力.
  • 未来的工作可以扩大这种方法,包括各种非英语科学文献在全球证据综合中.