ArticlePloS one2026
Comparing literature-guided, LLM-assisted, and data-driven models for predicting angler pressure.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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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.
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