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相关概念视频

Cognitive Learning01:21

Cognitive Learning

1.0K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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The Representativeness Heuristic02:13

The Representativeness Heuristic

16.7K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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相关实验视频

Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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专家知识诱导:访问专家大脑中的大数据.

Jacobo Robledo1,2,3,4, Aaron I Plex Sulá1,2,3,4, Lauren G Jaworski1,2,3,4

  • 1Plant Pathology Department, University of Florida, Gainesville, FL, U.S.A.

Phytopathology
|September 15, 2025
PubMed
概括

专家知识的提取使得宝贵的见解可用于植物健康挑战. 这种系统的方法综合了专家知识,为全球植物健康和疾病管理做出关键决策提供了信息.

关键词:
贝叶斯的更新是贝叶斯的更新.人工智能是一种人工智能.大数据就是大数据.决策支持提供了决策支持.流行病建模的流行病建模专家引发的诱惑专家的意见 专家的意见未来的场景 未来的场景存在的知识差距和知识缺口.自然语言处理自然语言处理.之前的知识 之前的知识

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相关实验视频

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科学领域:

  • 植物病理学 植物病理学
  • 农业科学 农业科学
  • 数据科学数据科学数据科学

背景情况:

  • 专家知识仍然在很大程度上无法访问数字系统,阻碍了植物病理学的及时决策.
  • 客观数据往往是不完整的,以应对紧急,不确定的或未来的植物健康挑战,如新出现的疾病.

研究的目的:

  • 探索专家知识获取对植物健康挑战的有效性.
  • 强调其在为基于专家的决策提供信息和与大数据流集成方面的作用.
  • 概述未来在全球植物卫生领域扩大专家知识获取规模的机会.

主要方法:

  • 系统的方法来访问和综合主题专家的见解.
  • 将专家知识视为大数据,与遥感,众包报告和数字监控集成.
  • 捕获输出作为可扩展数据集 (文本,表格,音频,视频) 用于人工智能支持的合成和贝叶斯分析.

主要成果:

  • 专家知识提取为解决复杂的植物病理问题和知识差距提供了有价值的数据.
  • 现实世界的实施提供了提取,结构化和解释专家衍生数据的教训.
  • 与大数据和人工智能的整合增强了理解不确定性的推断和透明度.

结论:

  • 专家知识的提取对于及时,基于专家的植物健康决策至关重要,特别是在不完整的数据的情况下.
  • 它可以通过与人工智能和大数据流的整合来扩展和加强.
  • 这种方法支持全球植物健康倡议,使专家的见解变得可行.