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Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Attribution Theory00:56

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Random Error01:04

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Updated: Jun 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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基于机器学习的极端事件归因.

Jared T Trok1, Elizabeth A Barnes2, Frances V Davenport3

  • 1Department of Earth System Science, Stanford University, Stanford, CA, USA.

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概括
此摘要是机器生成的。

机器学习,使用卷积神经网络,将极端天气事件归因于全球变暖. 这种方法提供了对气候变化对热浪影响的快速,低成本分析.

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科学领域:

  • 气候科学 气候科学
  • 机器学习 机器学习
  • 极端天气事件 极端天气事件

背景情况:

  • 全球气温上升正在增加极端天气事件的频率和强度.
  • 准确地将这些事件归因于人类造成的气候变化对于理解影响至关重要.
  • 已建立的归因方法可能耗时且资源密集.

研究的目的:

  • 开发和验证一种基于机器学习的新型方法,用于极端事件归因.
  • 量化全球平均温度 (GMT) 对特定极端高温事件的影响.
  • 评估机器学习提供快速和成本效益的事件归因的潜力.

主要方法:

  • 利用卷积神经网络 (CNN) 来创建动态一致的反事实场景.
  • 将CNN模型应用于最近北美中南部 (2023) 的极端高温事件和历史事件.
  • 将机器学习衍生的归因估计与已知方法的结果进行比较.

主要成果:

  • 据估计,2023年北美中南部热带活动期间的温度因全球变暖而增加1.18°C-1.42°C.
  • 预计类似事件每年发生0.140.60次,温度在工业化前GMT的2.0°C以上.
  • 每日温度和GMT之间的学习关系受季节性和每日气象条件的影响.

结论:

  • 机器学习,特别是CNN,为极端事件归因提供了可行的和高效的工具.
  • 这些发现与已建立的归因技术保持一致,验证了ML方法.
  • 这种方法可以更快,更容易地分析气候变化在极端天气中的作用.