Scientific models can be limited by oversimplification, as they often reduce complex systems to manageable components, potentially overlooking important interactions. They may rely on assumptions that do not hold true in all scenariOS, leading to inaccurate predictions. Additionally, models are constrained by the quality and availability of data, which can introduce bias or uncertainty. Finally, models may not account for unforeseen variables or changes in conditions, limiting their applicability over time.
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