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Fig. 4 | Animal Biotelemetry

Fig. 4

From: Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

Fig. 4

Individual variability of behaviours with respect to static acceleration in the Z-axis. Boxplot of each behavioural group (Rest, Alert, Presenting/Nursing (Nurse), Locomotion (Loco.), Comfort movements (CM), and Flippering pup (Flip. Pup)) with respect to static acceleration in the Z-axis (stZ) for torso-mounted accelerometers, the feature variable found to be most important in differentiating behaviour in the final random forest model. A high degree of variability existed between individuals and would likely contribute to a lower Precision and Recall when fitting random forests using pooled data

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