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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Rapid Agrichemical Inventory via Video Documentation and Large Language Model Identification.

Michael Anastario¹, Cynthia Armendáriz-Arnez², Lillian Shakespeare Largo¹

¹Department of Health Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA.

International Journal of Environmental Research and Public Health

|October 29, 2025

Summary

This summary is machine-generated.

This study introduces a method using large language models (LLMs) to quickly identify agrichemicals from video footage. This approach aids exposure assessments when researcher access is limited.

Keywords:

agrichemical identification avocado production exposure assessment large language models

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Area of Science:

Environmental Science
Occupational Health
Agricultural Science

Background:

Presents a novel methodological approach for agrichemical inventory documentation.
Complements exposure assessments with time-restricted observational methods in field settings.
Utilizes large language model (LLM) capabilities for agrichemical categorization.

Purpose of the Study:

To develop and evaluate a rapid method for documenting agrichemical inventories using LLMs.
To assess the feasibility of categorizing agrichemicals from video footage under time constraints.
To enhance exposure assessment strategies in field research.

Main Methods:

Recorded a short video of agrichemicals in a storage shed.
Processed video into 31 screenshots for analysis.
Employed OpenAI's ChatGPT (GPT-4o) for agrichemical identification and categorization.

Main Results:

LLM accurately identified 75% of agrichemicals.
Human verification was used to correct and validate LLM entries.
Demonstrated feasibility of LLM application in time-limited field research.

Conclusions:

The LLM-assisted method facilitates rapid initial data collection for exposure assessments.
This approach is valuable in situations with limited researcher access to hazardous materials.
LLM technology offers efficiency and cross-validation, enhancing field-based research capabilities.