ArticleScientific reports2026
Forecasting toxic metal concentrations in an inland sea ecosystem with machine learning algorithms.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
In recent years, statistical and data-driven modeling approaches have been increasingly employed to predict element concentrations and to examine relationships among environmental features. In this context, the integration of feature selection techniques with machine learning models enhances model generalization and reduces model complexity by enabling the identification of key elements that are strongly associated with the target feature. This study applies machine learning models to investigate the relationships between Aluminum (Al) and other elements and to predict Al concentration levels in an inland marine ecosystem. Specifically, the study evaluates whether accurate predictions can be achieved using a reduced subset of informative elements rather than the full feature set. The findings demonstrate that machine learning methods, when combined with feature selection, can successfully predict Al concentrations while yielding more interpretable models based on a limited number of significant elements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.