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验证一个快速的算法杂草危险排名方法
Christopher E Buddenhagen1, Graeme Bourdôt2, Shona Lamoureaux2
1AgResearch, Ruakura Research Centre, Hamilton, New Zealand.
Pest management science
|September 9, 2025
概括
一个新的算法杂草风险评估 (WRA) 模型,CPG,使用气候,出版和发生数据来快速选植物. 与传统方法相比,这种人工智能辅助工具节省了时间和资源,改善了全球生物安全.
科学领域:
- 生态生态学 生态生态学
- 侵袭性物种管理 侵袭性物种管理
- 计算生物学 计算生物学
背景情况:
- 传统的杂草风险评估 (WRA) 是资源密集型的,并且受到数据限制的阻碍.
- 提出了一个算法替代方案,即CPG模型,将气候适应性 (C),出版频率 (P) 和全球发生数据 (G) 整合起来.
研究的目的:
- 开发和验证用于早期杂草危害查的新,高效和可扩展的算法工具.
- 为了减少与传统WRAs相关的时间和成本.
主要方法:
- CPG模型使用公开可用的数据库和人工智能 (AI) 辅助的文本选,使用大型语言模型 (LLM).
- 模型的性能与新西兰和加利福尼亚州的独立杂草危害分类进行了验证,并与现有的风险评估模型进行了比较.
主要成果:
- 在新西兰,CPG模型成功地为89%的种类生成了分数,显示了与专家分类的良好协调以及与48个问题WRA的中度一致.
- 该模型与19个标准的加利福尼亚杂草危害系统和一致的预测性能 (真正阳性率0.69-0.90,真负率0.71-0.97) 强烈一致.
- 敏感性分析证实了得分稳定性和高危物种的强大排名.
结论:
- CPG模型为早期杂草危害查提供了一个透明,可扩展和具有成本效益的解决方案.
- 与传统的WRA相比,它大大减少了评估时间,同时保持了准确性和专家一致性.
- 该模型促进了对物种进行进一步评估的快速确定优先级,有助于全球生物安全和入侵物种管理工作.
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