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February 8, 2026Journal of Wood ScienceOpen Access

Machine learning-driven research in wood science: from prediction to understanding through the framework of Wood Informatics

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Authors

JBJunsik Bang

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Overview

This review synthesizes machine learning challenges in wood science, highlighting needs for data, models, and interpretability.

Key Points

  • The research aims to explore the application of machine learning in wood science and identify its challenges.
  • Review of existing literature on machine learning applications in wood science
  • Assessment of data heterogeneity and model interpretability
  • Introduction of the Wood Informatics framework for integration
  • Identified challenges in data heterogeneity and model generalization
  • Highlighted the gap in model interpretability aligned with physical mechanisms
  • Proposed Wood Informatics as a framework to improve data standardization and model reliability

Cite This Study

Junsik Bang (2026) studied this question.

synapsesocial.com/papers/698828530fc35cd7a8847b9chttps://doi.org/10.1186/s10086-026-02258-9
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