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Code, data and web application for: From laboratory isotherms to rivers: machine learning prediction of copper adsorption on microplastics, field scaling and LLM-assisted explanation
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This is a dataset and code release, not a new experiment. It combines laboratory and river measurements of copper sticking to microplastics, machine learning models predicting this process, and tools (including a web app) that explain the predictions using an AI language system.
Curated literature dataset (961 laboratory observations from 32 studies; field records of Cu on riverine microplastics with paired water Cu), Python workflow and result tables for the study-grouped validation of machine-learning and isotherm-form models of Cu adsorption on microplastics, the empirical field-to-laboratory ratio, river-scale and global estimates, and the evaluation of an LLM explanation system (rule-based, retrieval and retrieval + knowledge graph; LLM judges and expert scores). Includes the Streamlit web application (src/ui/app.py). Full-text articles and the retrieval index derived from them are not included because they cannot be redistributed.
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