Marco Baity‐Jesi

This paper identifies criteria for reliable comparison between predictive ecotoxicology studies and highlights the lack of compliance with these criteria in existing research.

This study provides a high-frequency dataset from Lake Greifen, integrating physical, chemical, and biological parameters to study lake dynamics and plankton ecology.

The paper shows biotic interactions significantly alter the realised niche of toxic cyanobacteria, affecting temperature and phosphorus thresholds for bloom formation.

This study evaluates the sensitivity of daphnids compared to fish for neurotoxic chemicals, finding daphnids often more sensitive and suggesting their use as an alternative to fish toxicity tests.

The paper uses Random Forest models on four years of lake data to show temperature and microzooplankton grazing significantly influence phytoplankton size class dynamics.

This study trains and evaluates machine learning models to predict fish acute mortality from chemical properties, finding that tree-based models perform best but struggle with individual chemical predictions.

The paper introduces ADORE, a benchmark dataset for machine learning in ecotoxicology, to facilitate model comparison and challenge researchers to improve predictive performance.