Marco Baity‐Jesi

On the Comparability between Studies in Predictive Ecotoxicology

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

Five years of high-frequency data of phytoplankton zooplankton and limnology from a temperate eutrophic lake

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

Biotic interactions shape the realised niche of toxic cyanobacteria

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

Daphnids Can Safeguard the Use of Alternative Bioassays to the Acute Fish Toxicity Test: A Focus on Neurotoxicity

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.

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

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.

A Benchmark Dataset for Machine Learning in Ecotoxicology

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