Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

565
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
565
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

745
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
745
Random Error01:04

Random Error

937
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...
937
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

68
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...
68
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

200
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
200
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.6K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Biotic interactions biogeography: A framework for understanding how species interactions shape biodiversity patterns across scales.

PLoS biology·2026
Same author

Oceanographic connectivity strongly restricts future range expansions of critical marine forest species.

npj biodiversity·2026
Same author

Long-term warming reduces fish biomass, but heatwaves shift it.

Nature ecology & evolution·2026
Same author

Microbiota-derived IPA protects against colitis by regulating intestinal HMGCS2-mediated ketogenesis to facilitate mucosal healing.

Nature communications·2026
Same author

Integrative functional genomics analysis identifies pleiotropic genes for vascular diseases.

Nature communications·2026
Same author

Remote sensing enables rapid assessment of the March 28, 2025 Myanmar earthquake.

Science bulletin·2026

相关实验视频

Updated: Jul 28, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

在堆叠的生物气候信封模型中数据错误的传播.

Xueyan Li1, Babak Naimi2, Peng Gong3

  • 1Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Science, Guangzhou, China.

Integrative zoology
|June 1, 2023
PubMed
概括
此摘要是机器生成的。

不同的数据源和生物气候外模型 (BEMs) 创造了不同的物种丰富模式. 公民科学数据产生了准确的模型,而范围地图对于生物多样性评估来说最不准确.

关键词:
财富模式 财富模式种类分布 种类分布堆叠的生物气候信封模型.不确定性是一种不确定性.

更多相关视频

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Jul 28, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 生态生态学 生态生态学
  • 生物多样性研究 生物多样性研究
  • 计算生物学 计算生物学

背景情况:

  • 物种丰富性模式对于理解生态过程至关重要.
  • 生物气候外模型 (BEMs) 被广泛用于推断物种分布.
  • 在物种分布建模中的数据限制可能会影响生态推断.

研究的目的:

  • 调查不同数据源对中国估计物种丰富度梯度的影响.
  • 为了比较各种BEM和生物多样性评估数据类型的准确性和稳定性.

主要方法:

  • 用全球范围地图,区域检查清单,博物馆记录和公民科学数据为334种鸟类安装了BEM.
  • 雇员仅存在,存在背景和存在缺席的BEM (Mahalanobis距离,MAXENT,GAM,BRT).
  • 堆叠单个物种预测,以产生物种丰富度梯度,并进行灵敏度分析.

主要成果:

  • 根据数据来源和使用的BEM,物种丰富度梯度有很大差异.
  • 公民科学数据导致模型精度最高;全球范围地图的精度最低.
  • 解释物种分布的环境预测因素在数据来源之间存在差异.
  • GAM和BRT模型显示出对数据不确定性的稳定性.

结论:

  • 当多个数据集可用时,通过敏感性分析明确解决数据不确定性至关重要.
  • 数据来源和BEM的选择显著影响生物多样性模式的推断.
  • 公民科学数据为物种分布建模提供了有价值的,高精度的资源.