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This paper builds upon Monte Carlo simulation methods to investigate whether incorporating crop rotation information—encoded as a Markov chain—can improve identification accuracy and concludes that incorporating accurate prior information and crop rotation models noticeably improves crop identification performance.
It is shown that the proposed TAME method consistently surpasses baselines on neural classifiers, while remaining competitive with strong coreset baselines on tree-based classifiers (RF, XGBoost) with a lightweight validation method.
These results establish fine-grained body language as a critical debiasing signal, recovering accuracy on the subtle expressions that text and speech alone fail to capture.
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