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    Sociality predicts orangutan vocal phenotype

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    Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles

    Of which at the third instar, the external morphology of larvae is quite similar; thus, the morphological identification used to differentiate between its genera or species, generally includes cephalophalyngeal skeleton, anterior spiracle, and posterior spiracles. The morphology of the posterior spiracle is one of the important characteristics for identification. A typical morphology of the posterior spiracle of third stage larvae was shown in Fig. 2. Based on studying under light microscopy, the posterior spiracle of M. domestica was clearly distinguished from the others. On the other hand, the morphology of the posterior spiracle of C. megacephala and A. rufifacies was quite similar. For C. megacephala and C. rufifacies, the peritreme, a structure encircling the three spiracular openings (slits), was incomplete and slits were straight as shown Fig. 2A,B, respectively. The complete peritreme encircling three slits was found in L. cuprina and M. domestica as shown in Fig. 2C,D, respectively. However, only the slits of M. domestica were sinuous like the M-letter (Fig. 2D). Their morphological characteristics found in this study were like the descriptions in the previous reports23,24,25.Figure 2Morphology of posterior spiracles of four different fly species after inverting the image colors; (A) Chrysomya (Achoetandrus) ruffifacies, (B) Chrysomya megacephala, (C) Lucilia cuprina, (D) Musca domestica.Full size imageFor model training, four of the CNN models used for species-level identification of fly maggots provided 100% accuracy rates and 0% loss. Number of parameter (#Params), model speed, model size, macro precision, macro recall, f1-score, and support value were also presented in Table 1. The result demonstrated that the AlexNet model provided the best performance in all indicators when compared among four models. The AlexNet model used the least number of parameters while the Resnet101 model used the most. For model speed, the AlexNet model provided the fastest speed, while the Densenet161 model provided the slowest speed. For the model size, the AlexNet model was the smallest, while the Resnet101 model was the largest which corresponded to the number of parameters used. Macro precision, macro recall, f1-score and support value of all models were the same.Table 1 Comparison of model size, speed, and performances of each studied model (The text in bold indicates the best value in each category).Full size tableAs the training results presented in the supplementary data (Fig. S1), all models provided 100% accuracy and 0% loss in the early stage of training ( More

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    European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.)

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    Cultural diversity through the lenses of ecology

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    Forest structure determines nest box use by Central European boreal owls

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