2 resultados para tagging

em Repositório Científico da Universidade de Évora - Portugal


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Mark-recapture tagging and acoustic telemetry were used to study the movements of Diplodus sargus within the Pessegueiro Island no-take Marine Protected Area (MPA), (Portugal) and assess its size adequacy for this species' protection against fishing activities. Therefore, 894 Diplodus sargus were captured and marked with conventional plastic t-bar tags. At the same time, 19 D. sargus were tagged with acoustic transmitters and monitored by 20 automatic acoustic receivers inside the no-take MPA for 60 days. Recapture rate of conventionally tagged specimens was 3.47%, most occurring during subsequent marking campaigns. One individual however was recaptured by recreational fishermen near Faro (ca. 250 km from the tagging location) 6 months after release. Furthermore, three specimens were recaptured in October 2013 near releasing site, one year after being tagged. Regarding acoustic telemetry, 18 specimens were detected by the receivers during most of the study period. To analyse no-take MPA use, the study site was divided into five areas reflecting habitat characteristics, three of which were frequently used by the tagged fish: Exterior, Interior Protected and Interior Exposed areas. Information on no-take protected area use was also analysed according to diel and tidal patterns. Preferred passageways and permanence areas were identified and high site fidelity was confirmed. The interaction between tide and time of day influenced space use patterns, with higher and more variable movements during daytime and neap tides. This no-take MPA proved to be an important refuge and feeding area for this species, encompassing most of the home ranges of tagged specimens. Therefore, it is likely that this no-take MPA is of adequate size to protect D. sargus against fishing activities, thus contributing to its sustainable management in the region.

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This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic tag- gers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and F1. Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntac- tic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achiev- ing (in almost every verb) an improvement in F1 when compared to the traditional bow approach.