{
  "abstract": "Aquatic ecosystems are threatened by thousands of chemicals whose hazard is often unknown. Laboratory tests of individual chemicals on individual species establish, for example, the Lethal Concentration for 50% of test individuals (LC50), but for most chemicals in use such data exist for only a small fraction of species. A growing body of research applies machine learning to bridge these gaps. Before such models can be relied on in regulatory and environmental management contexts, they must not only be accurate in general, but also quantify how far individual predictions can be trusted. We address this by training a Bayesian Factorization Machine (BFM) on ecotoxicity test data. The BFM matches the accuracy of the current state of the art in predicting LC50 values, and additionally quantifies the uncertainty of every prediction, separating the part arising from a lack of data (epistemic) from the part that is irreducible, reflecting inter-test and biological variability (aleatoric). We then show how this uncertainty propagates into environmental quality assessment: Species Sensitivity Distributions (SSDs) become ensembles of plausible curves rather than single fits, and the hazardous concentrations derived from them carry credible intervals that are widest for the data-poor chemicals where conventional SSDs are least reliable.",
  "abstract_shown": true,
  "affiliations": [
    "National Institute for Public Health and the Environment",
    "Department of Mathematics, Computer Science and Statistic",
    "Ghent University",
    "Leiden University",
    "Leiden Institute of Advanced Computer Science",
    "Department of Environmental Science",
    "Radboud University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tadeusz Price"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Oliver Urs Lenz"
    },
    {
      "affs": [
        0
      ],
      "name": "Markus Viljanen"
    },
    {
      "affs": [
        0,
        5,
        6
      ],
      "name": "Leo Posthuma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6mjaz7aqhb1h2.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009892/v1",
  "fulltext_url": null,
  "id": "6mjaz7aqhb1h2",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009892/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2307|Environmental Toxicology and Ecotoxicology"
    ]
  ],
  "openalex": "W7220473189",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009892/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6mjaz7aqhb1h2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15009892",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009892/v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2307|Environmental Toxicology and Ecotoxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Uncertainty Quantification for Aquatic Ecotoxicity Prediction",
  "title_text": "Uncertainty Quantification for Aquatic Ecotoxicity Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/6mjaz7aqhb1h2",
  "venue": "ChemRxiv",
  "versions": []
}