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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Marine & Wildlife Policy & Risk Sign in to save

Scaling laboratory results with machine learning is no silver bullet to strengthen global (micro-)plastic mitigation policy: [Dataset]

OpenAgrar 2025
Elke Brandes, Peter Fiener, Peter Fiener, Arthur Gessler, Arthur Gessler

Summary

This commentary examines the limitations of using machine learning to scale laboratory microplastic photosynthesis-inhibition results to global estimates, arguing that such approaches are insufficient to reliably inform international plastic mitigation policy without addressing underlying data and model uncertainties.

Supplementary Information for letter to the editor / commentary concerning the article “A global estimate of multiecosystem photosynthesis losses under microplastic pollution” (Zhu et al., 2025) https://doi.org/10.1073/pnas.2423957122

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