Validating a rapid algorithmic weed hazard ranking method.

ABSTRACT:

Background

Conventional weed risk assessments (WRAs) are time-consuming and often constrained by species-specific attribute data gaps. We present a validated, algorithmic alternative, the CPG model, that integrates climatic suitability ( C ), weed-related publication frequency (P) and global occurrence data ( G ), using publicly available databases and artificial intelligence (AI)-assisted text screening with a large language model (LLM).

Results

The CPG model was tested against independent weed hazard classifications for New Zealand and California. In New Zealand, 89% of 480 randomly selected plant taxa had sufficient data to generate scores, which aligned well with expert classifications and moderately with outputs from the 48-question WRA [Pheloung PC, Williams PA, and Ha

SUBMITTER: Buddenhagen CE 

PROVIDER: S-EPMC12713730 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature