Rapid Agrichemical Inventory via Video Documentation and Large Language Model Identification
Bibliographic Data
| ID | 15469381 |
|---|---|
| Authors | Mike Anastario (0000-0002-7761-572X, Northern Arizona University, corresponding author), Cynthia Armendáriz-Arnez (0000-0002-6437-0671, Universidad Nacional Autonoma de Mexico), Lillian Shakespeare Largo (Northern Arizona University), Talia Gordon (University of Michigan), E F S Roberts (0000-0003-1025-1236, University of Michigan) |
| Year | 2025 |
| Volume | 22 |
| Issue | 10 |
| Pages | 1527-1527 |
| Publication date | 2025-10-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph22101527 |
| PMID | 41154931 |
| OpenAlex | W4414847887 |
| Language | EN |
| References cited | 18 |
This rapid identification method builds upon behavioral methods of exposure assessment, facilitating initial data collection in contexts where researcher access to hazardous materials may be time limited and would benefit from the efficiency and cross-validation offered by this method. Further refinement of this LLM-assisted approach could optimize accuracy in the identification of agrichemical products and expand its application to complement exposure assessments in field-based research, particularly as LLM technologies rapidly evolve. Most importantly, this Technical Note illustrates how field researchers can strategically harness LLMs under real-world time constraints, opening new possibilities for rapid observational approaches to exposure assessment
Agrochemical · Complement (music · Data collection · Documentation · Field (mathematics · Hazardous waste · Identification (biology · Observational study · Animal Disease Management and Epidemiology · Data-Driven Disease Surveillance · Zoonotic diseases and public health
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| Highly cited | No |
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