Quantifying allo-grooming in wild chacma baboons (<i>Papio ursinus</i>) using tri-axial acceleration data and machine learning.

As the correlations were strong ( ρ range: 0.77–0.92) and highly significant ( p ≤ 0.005 for all behaviours; electronic supplementary material, figure S2), all focal data were included to maximize the amount of data used in the analysis.

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