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相关概念视频

Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
181
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

39
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Stratified Sampling Method01:16

Stratified 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. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

313
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
313
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Updated: Jun 29, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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综合距离采样模型用于简单的点数计数.

Marc Kéry1, J Andrew Royle2, Tyler Hallman1,3,4

  • 1Swiss Ornithological Institute, Sempach, Switzerland.

Ecology
|March 27, 2024
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概括
此摘要是机器生成的。

综合远距离采样 (IDS) 模型将远距离采样与点数或检测/不检测数据相结合,以准确估计野生动物密度,并考虑到生物多样性调查中的可检测性偏差.

关键词:
丰富的 丰富的 丰富的可用性概率可用性概率生物多样性监测 生物多样性监测公民科学是公民科学.社区科学 社区科学检测/不检测数据距离采样采样 距离采样采样集成模型的综合模型.参与式科学 参与式科学感知度 感知度 感知度积分计数数据 积分计数数据 积分计数数据

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科学领域:

  • 生态生态学 生态生态学
  • 野生动物生物学 野生动物生物学
  • 统计建模 统计建模

背景情况:

  • 点数计数 (PC) 是常见的生物多样性调查方法,但受到未知的检测能力的影响,导致偏见的丰度估计.
  • 可检测度的时空变化和未知的调查区域阻碍了准确的密度估计和景观层面的缩放.
  • 现有的公民科学数据往往缺乏信息来纠正检测偏差,从而限制了它们的生态效用.

研究的目的:

  • 引入综合距离采样 (IDS) 模型,以解决传统点数和检测/不检测数据的局限性.
  • 通过结合多种数据类型,实现精确的密度估计和景观层次推断.
  • 通过纠正检测偏差来提高公民科学数据的实用性.

主要方法:

  • IDS模型将距离采样 (DS) 与点计数 (PC) 或检测/不检测 (DND) 数据相结合.
  • 将PC和DND数据视为潜伏的DS调查的汇总,以估计单独的检测功能和共变效应.
  • 使用重复或时间移除调查来估计可用性和可感知性的可检测性组件.

主要成果:

  • IDS模型调和不同数据集之间的空间和时间不匹配.
  • 成功地解决了简单PC和DND数据中固有的可检测性和调查区域问题.
  • 提供 JAGS 代码和一个 R 包函数 ("IDS") 来安装这些模型.

结论:

  • IDS模型提供了一个强大的解决方案,用于在生态调查中准确估计密度.
  • 通过纠正检测偏差,显著扩大公民科学数据的实用性和覆盖范围.
  • 适用于混合调查设计,将DS与无距离方法相结合,对生态和保护管理具有广泛的影响.