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• W4213021844 endingPage "520" @default.
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• W4213021844 abstract "Monitoring reproductive outputs of sea turtles is difficult, as it requires a large number of observers patrolling extended beaches every night throughout the breeding season with the risk of missing nesting individuals. We introduce the first automatic method to remotely record the reproductive outputs of green turtles (Chelonia mydas) using accelerometers. First, we trained a fully convolutional neural network, the V-net, to automatically identify the six behaviors shown during nesting. With an accuracy of 0.95, the V-net succeeded in detecting the Egg laying process with a precision of 0.97. Then, we estimated the number of laid eggs from the predicted Egg laying sequence and obtained the outputs with a mean relative error of 7% compared to the observed numbers in the field. Based on deployment of non-invasive and miniature loggers, the proposed method should help researchers monitor nesting sea turtle populations. Furthermore, its use can be coupled with the deployment of accelerometers at sea during the intra-nesting period, from which behaviors can also be estimated. The knowledge of the behavior of sea turtle on land and at sea during the entire reproduction period is essential to improve our knowledge of this threatened species." @default.
• W4213021844 created "2022-02-24" @default.
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• W4213021844 date "2022-02-20" @default.
• W4213021844 modified "2023-10-10" @default.
• W4213021844 title "Estimation of the Maternal Investment of Sea Turtles by Automatic Identification of Nesting Behavior and Number of Eggs Laid from a Tri-Axial Accelerometer" @default.
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• W4213021844 doi "https://doi.org/10.3390/ani12040520" @default.
• W4213021844 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35203228" @default.
• W4213021844 hasPublicationYear "2022" @default.
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