ArticleEnvironmental science and pollution research international2017
Examining predictors of chemical toxicity in freshwater fish using the random forest technique.
Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- G.AI.A: An Integrated Machine-Learning Platform for Predicting Bioaccumulation and Ecotoxicity of Pharmaceuticals.Journal of chemical information and modeling · 2026Article
- A benchmark dataset for machine learning in ecotoxicology.Scientific data · 2023Article
- Key factors explaining critical swimming speed in freshwater fish: a review and statistical analysis for Iberian species.Scientific reports · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Chemical pollution is one of the main issues globally threatening the enormous biodiversity of freshwater ecosystems. The toxicity of substances depends on many factors such as the chemical itself, the species affected, environmental conditions, exposure duration, and concentration. We used the random forest technique to examine the factors that mediate toxicity in a set of widespread fishes and analyses of covariance to further assess the importance of differential sensitivity among fish species. Among 13 variables, the 5 most important predictors of toxicity with random forests were, by order of importance, the chemical substance itself (i.e., Chemical Abstracts Service number considered as a categorical factor), octanol-water partition coefficient (log P), pollutant prioritization, ecological structure-activity relationship (ECOSAR) classification, and fish species for 50% lethal concentrations (LC
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