A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.

开发了一种混合贝叶斯网络（BN），它使用鱼胚毒性（FET）测试数据和其他信息来预测化学品对鱼类的急性毒性。该模型可以支持使用证据权重（WOE）方法替代场对幼鱼的FET数据。BN预测了69％–80％的被测物质的正确毒性间隔。该模型对通过毒性数据量化的成分最敏感，而对通过专家知识量化的成分最不敏感。该模型可通过Web界面公开获得。此模型的进一步开发应包括更多证据线，细化离散度，以及使用更大的数据集进行训练以对证据线进行加权。