Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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

Understanding mammal avoidance of human settlements.

The Journal of animal ecology·2026

Same author

Evaluation of Pronghorn (Antilocapra americana) Chemical Immobilization in a Captive Research Facility in Wyoming, USA (2021-24).

Journal of wildlife diseases·2026

Same author

Co-occurrence patterns of internal parasitic nematodes and coccidia in wild bighorn sheep depend on host geography and environment.

International journal for parasitology·2026

Same author

Interacting effects of human presence and landscape modification on birds and mammals.

Science (New York, N.Y.)·2026

Same author

Resolving the Evolutionary History of Bighorn Sheep to Inform Future Management: An Answer to the California Bighorn Lineage Question.

Evolutionary applications·2026

Same author

In cold blood: screening wild Alaskan moose (<i>Alces alces gigas</i>) for filarial nematode (Filarioidea: Onchocercidae) infections using a deep-amplicon sequencing approach.

Parasitology·2026

Search research articles

相关实验视频

Updated: Jan 8, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

加速度计行为:R包用于将类动物的行为分类为三个状态.

Rachel A Smiley^1,2, Seth T Rankins^1,2, Lindsay Millward³

¹Haub School of the Environment and Natural Resources University of Wyoming Laramie Wyoming USA.

Ecology and evolution

|December 22, 2025

概括

此摘要是机器生成的。

研究人员开发了一个R包,加速计行为,使用GPS项圈的加速计数据对类动物的行为进行分类. 该工具增强了用于野生动物研究的未充分利用的移动数据的使用.

关键词:

加速度计的加速度计.活动预算活动预算活动数据活动数据这是一只大角羊.鹿鹿鹿鹿鹿鹿鹿鹿是是是是是是是鹿鹿鹿鹿鹿鹿鹿鹿鹿类动物类动物

更多相关视频

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Published on: January 3, 2017

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

相关实验视频

Last Updated: Jan 8, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Published on: January 3, 2017

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

科学领域:

野生动物生态学野生动物生态学
动物行为动物行为
运动生态学运动生态学

背景情况:

技术的进步使得精细规模的动物行为研究成为可能.
来自GPS项圈的加速度计数据在行为分析中未得到充分利用.
根据加速度计数据对类动物的行为进行分类,需要强大的模型.

研究的目的:

开发和验证模型来分类类动物的行为 (静止,食,旅行) 使用加速度计数据.
为了创建一个R包 (加速计行为) 可访问的应用这些模型.
将从加速度计数据中得出的活动预算与基于GPS的隐马尔科夫模型中的活动预算进行比较.

主要方法:

配对的加速度计数据与三种类动物的直接行为观测.
开发随机森林模型用于行为分类.
验证了模型,并创建了适用于缺乏观测数据的物种的一般类动物模型.

主要成果:

在特定物种的模型中实现了高分类精度 (≥87%) 和AUC (≥0.93).
开发了一种具有90%准确度和0.95AUC的一般类模型.
加速度计行为R包允许直接应用验证的模型.

结论:

加速度计行为方便低利用的加速度计数据用于类动物的行为研究.
方法和数据解决方案对活动预算估计产生重大影响.
R包简化了行为分类,扩大了研究潜力.