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- W3037801811 abstract "Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research. Successful predictive models are typically accurate, reliable, and transparent regarding their assumptions and expectations, indicating high predictive capacity, robustness, and clarity in their objectives and standards. Research on improving these properties is becoming more common, but often individual research projects are focused on a single aspect of the modelling process and are typically disseminated only within the field where the research originated. The goal of this review is to synthesize research from various disciplines and topics to provide a coherent framework for developing efficient predictive models. Our framework summarizes the process of creating predictive models into three main stages: (1) Framing the Question; (2) Model-Building and Testing; and (3) Uncertainty Evaluation with proposed strategies associated with each stage to help produce more successful predictive models. The key strategies identified within our framework form specific guidelines, providing a new perspective to help researchers make predictive modelling more accurate, reliable, and transparent." @default.
- W3037801811 created "2020-07-02" @default.
- W3037801811 creator A5007892132 @default.
- W3037801811 creator A5037446538 @default.
- W3037801811 creator A5046716302 @default.
- W3037801811 date "2020-06-01" @default.
- W3037801811 modified "2023-10-11" @default.
- W3037801811 title "Making predictive modelling ART: accurate, reliable, and transparent" @default.
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- W3037801811 doi "https://doi.org/10.1002/ecs2.3160" @default.
- W3037801811 hasPublicationYear "2020" @default.