Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Equally Effective: Comparing ChatGPT, Literature Guided, and Data-Driven Models in Predicting Angler Pressure
0
Zitationen
6
Autoren
2025
Jahr
Abstract
This study compared three models–a literature-guided, a ChatGPT-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 angler-reported variables on online platforms, including the number of fishing trips, fishing duration, catch rate, and lake webpage views. Each model was evaluated using 10-fold cross-validation, and the results showed no significant difference in predictive accuracy. All three models identified webpage views as a key predictor of the number of boats. These findings underscore the potential of AI-driven approaches, as the ChatGPT-assisted model performed on par with literature-guided and data-driven models, demonstrating its viability for ecological predictions.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.700 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.605 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.133 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.873 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.