相关实验视频
Updated: Jun 8, 2026
08:02
Robotic Sensing and Stimuli Provision for Guided Plant Growth
Published on: July 1, 2019
校正:阿尔马多尔等人. 由人工智能驱动的框架,通过来自DSLR相机传感器的基于高分辨率图像的机器学习来识别瓜植物疾病. 传感器2021 年,3830 年,3830 年
Ahmad Almadhor1, Hafiz Tayyab Rauf2, Muhammad Ikram Ullah Lali3
1Department of Computer Engineering, Networks Jouf University, Sakaka 72388, Saudi Arabia.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究纠正了科学出版物的附属关系. 确保准确的作者归因对于研究完整性和协作跟踪至关重要.
科学领域:
- 图书统计学 图书统计学
- 学术传播学术交流
- 研究诚信研究诚信
背景情况:
- 准确的作者归属对于跟踪研究影响和合作至关重要.
- 关联的差异可能会阻碍准确的科学记录.
- 建立正确的机构联系对于文献计量分析至关重要.
研究的目的:
- 解决和纠正科学文献中作者归属的错误.
- 提高发表的研究中机构归因的准确性.
- 提高用于圣经计量和科学计量研究的数据的可靠性.
主要方法:
- 使用数据库交叉引用和机构验证协议.
- 实施系统审查流程,以识别和纠正关联数据.
- 采用数据清理技术来标准化关联信息.
主要成果:
- 成功纠正了大量出版物的附属数据.
- 提高了作者到机构映射的准确性.
- 为未来的研究分析建立了更可靠的数据集.
结论:
- 准确的附属数据对于科学出版的完整性至关重要.
- 实施的校正方法为保持数据质量提供了一个强大的框架.
- 纠正的归属性提高了文献计量和协作研究的价值.
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