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An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers
Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated adoi:10.1186/s40462-021-00245-x pmid:33785056 fatcat:agyn5dnh7ncopfri7vqeupaggq