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相关概念视频

Range00:59

Range

11.0K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
11.0K
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

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The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
8.9K
Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
3.6K
Interpreting R Charts01:22

Interpreting R Charts

57
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
57
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

926
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
926
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

64
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
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相关实验视频

Updated: Jun 14, 2025

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

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内部范围结构可以预测范围的变化吗?

Neil A Gilbert1,2, Stephen R Kolbe3, Harold N Eyster4,5

  • 1Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, USA.

The Journal of animal ecology
|September 2, 2024
PubMed
概括
此摘要是机器生成的。

由于气候变化导致的物种范围的变化是可变的. 这项研究发现,范围边缘硬度预测人口趋势的证据很弱,这表明不同的机制驱动范围扩张而不是收缩.

关键词:
生物地质学 生物地质学气候变化 气候变化 气候变化宏观生态学的宏观生态学范围的变化范围的变化种类分布,物种分布.

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科学领域:

  • 生态生态学 生态生态学
  • 气候变化生物学 气候变化生物学
  • 保护生物学 保护生物学

背景情况:

  • 由于气候变化,物种正在改变其地理范围,但预测这些变化仍然具有挑战性.
  • 功能性特征对预测范围转移的能力有限,需要探索其他范围特征.

研究的目的:

  • 测试该假设范围边缘硬度,定义为范围边缘的相对丰度,可以预测范围边界的人口趋势.
  • 调查关于范围边缘硬度和范围移动的惯性和限制假设.

主要方法:

  • 利用美国明尼苏达州北部长期鸟类监测数据集.
  • 估计35种尾端和18种前沿物种的种群趋势.
  • 模拟人口趋势作为范围边缘硬度的函数,来源于eBird数据.

主要成果:

  • 发现范围边缘硬度与范围边缘人口趋势之间存在联系的证据有限.
  • 带有较硬边缘的尾行边缘物种显示出轻微的下降趋势 (对限制假设的弱支持).
  • 具有更硬边缘的尖端物种显示出轻微的增长趋势 (对惯性假设的弱支持).

结论:

  • 领先和尾端的对立结果表明,不同的机制控制范围的扩张和收缩.
  • 范围边缘硬度显示人口趋势的预测能力较弱,表明范围转移动态的复杂性.
  • 数据和建模的未来进展可能会通过范围内的丰富性模式来改善范围转移的预测.