{
  "abstract": "This study compared three models–a literature-guided, a large language model (LLM)-assisted, and a data-driven model–all developed using Bayesian networks as the framework for predicting angler pressure measured by the number of boats observed in aerial surveys. The models used meteorological data from 98 lakes in Ontario, Canada, during 2018 and 2019, as well as platform-derived variables, including angler-reported fishing trips, fishing duration, and catch rate, together with lake webpage views. In five-fold cross-validation, no pairwise difference in fold-level accuracy among the three models remained significant after Holm adjustment. These findings suggest that LLM-assisted model construction has potential to support ecological prediction when paired with empirical validation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada",
    "Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada",
    "Angler’s Atlas, Goldstream Publishing, Prince George, British Columbia, Canada",
    "Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada",
    "Departments of Mathematics and Statistics and Biology, University of Victoria, Victoria, British Columbia, Canada",
    "Department of Mathematics and Statistics and Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Azar Taheri Tayebi"
    },
    {
      "affs": [
        1
      ],
      "name": "Julia S. Schmid"
    },
    {
      "affs": [
        2
      ],
      "name": "Sean Simmons"
    },
    {
      "affs": [
        3
      ],
      "name": "Mark S. Poesch"
    },
    {
      "affs": [
        4
      ],
      "name": "Mark A. Lewis"
    },
    {
      "affs": [
        5
      ],
      "name": "Pouria Ramazi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4r7fdrzd1ap5b.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357450",
  "fulltext_url": null,
  "id": "4r7fdrzd1ap5b",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357450",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2309|Fish Ecology and Management Studies"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture/Aquaculture/Fisheries"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Behavior/Recreation"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Humidity"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Wind"
    ],
    [
      "plos",
      "/Engineering and technology/Structural engineering/Built structures"
    ],
    [
      "plos",
      "/Engineering and technology/Transportation/Boats"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Behavior/Recreation"
    ]
  ],
  "openalex": "W7221157474",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357450&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/4r7fdrzd1ap5b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0357450",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357450"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    }
  ],
  "title": "Comparing literature-guided, LLM-assisted, and data-driven models for predicting angler pressure",
  "title_text": "Comparing literature-guided, LLM-assisted, and data-driven models for predicting angler pressure",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/4r7fdrzd1ap5b",
  "venue": "PLOS One",
  "versions": []
}