848 resultados para competition interval
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In the present study, data of 1,578 first lactation females, calving from 1985 to 2006 were analysed with the purpose of estimating genetic parameters for milk yield (MY), age at first calving (AFC) and interval between first and second calving (IBFSC) in dairy buffaloes of the Murrah breed in Brazil, Heritability estimates for MY, AFC and IBFSC traits were 0.20, 0.07 and 0.14, respectively. Genetic correlations between MY and AFC and IBFSC were -0.12 and 0.07, respectively, while the corresponding phenotypic correlations were -0.15 and 0.30, respectively. Genetic and phenotypic correlations between AFC and IBFSC were 0.35 and 0.37, respectively. Genetic correlation between MY and AFC showed desirable negative association, suggesting that daughters of the bulls with high breeding values for MY could reach physiological mature at a precocious age. Genetic correlation between MY and IBFSC, showed that the selection for milk production could result in the increase of calving intervals.
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Best practices in defence of competition in Argentina and Brazil: useful aspects for Central America
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Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.
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