Mineração de dados e estimativa da mortalidade alta de frangos quando expostos a onda de calor

As ondas de calor provocam perdas na produção animal pela sua exposição ao estresse por calor aumentando a mortalidade, e consequentemente, perdas econômicas. Bancos de dados zootécnicos e meteorológicos históricos podem conter informações que permitem modelar a mortalidade de frangos devido à incidência de ondas de calor. O objetivo foi analisar bancos de dados de frangos de corte associados a dados meteorológicos utilizando técnicas de mineração de dados, seleção de atributos e classificação (árvore de decisão) para modelar o impacto da incidência de onda de calor na mortalidade de frangos de corte. O Índice de Temperatura e Umidade (ITU) foi utilizado para descrever parte dos dados ambientais. A técnica de Mineração de Dados permitiu a construção de três modelos compreensíveis para estimar a alta mortalidade em frangos de corte. Os modelos gerados pela abordagem de seleção de atributos por Análise dos Componentes Principais e Wrapper apresentaram igual desempenho com uma precisão total de 89,3% e a classificação para alta mortalidade foi de 83,3%. Quando a seleção foi feita por especialistas do domínio, a precisão do modelo foi de 85,7%, e a da classificação para alta mortalidade foi de 76,9%. Resultados meteorológicos e o ITU calculada a partir de estações meteorológicas permitiram identificar condições ambientais prejudiciais para frangos entre 29 e 42 dias de vida. A técnica de Mineração de Dados é aplicável para construir modelos preditivos para a produção animal.Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models

Multimodalidade e Letramento visual crítico no ensino de Língua Inglesa: Investigando as propostas de atividade de produção de texto do livro didático Time to Share

This article aims to investigate the English teaching as an Additional Language through the writing activities from the textbook Time to share (AGA; MARTÍNEZ, 2015), of the teachers` book, from ninth level from elementary school to verify how writing production, which integrates languages ​​and semiosis, can reveal critical visual / multimodal literacy. This work is maintained by the multimodality studies by Kress and Van Leeuwen (1996, 2006), Jewitt (2009), Bezemer and Kress (2008, 2016); based on critical visual literacy by Callow (2008, 2012, 2013) and on texts composition approaches purposed by Ribeiro (2016, 2018). The results reveal that the writing activities proposals of the 9th grade textbook bring relevant themes to work in English teaching. However, when it comes to textual production, in addition to the verbal mode, the analysis revealed that the book presents an insufficient approach concerning the texts that integrate words and images, despite having a visually attractive appearance. Consequently, the textbook ends up limiting the literacy agencies to the written system.Este artigo objetiva investigar o ensino de Língua Inglesa como Língua Adicional (LA) por meio das atividades de produção textual do Livro Didático Time to share (AGA; MARTÍNEZ, 2015), do livro do professor do 9º ano do Ensino Fundamental, para verificar como o ensino de produção de texto, que integra linguagens e semioses, pode revelar letramento visual crítico. Este trabalho está respaldado nos estudos da multimodalidade de Kress e Van Leeuwen (1996, 2006), Jewitt (2009), Bezemer e Kress (2008, 2016); do letramento visual crítico de Callow (2008, 2012, 2013) e em abordagens para o ensino de produção de texto propostas por Ribeiro (2016, 2018). Os resultados revelaram que as propostas de produção de texto do livro do 9º ano trazem temáticas relevantes para se trabalhar no ensino de inglês. Entretanto, quando se trata de produção textual, para além do modo verbal, a análise revelou que o livro apresenta uma abordagem insuficiente com relação aos textos que integram palavras e imagens, apesar de ter uma aparência visualmente atraente. Assim, o livro didático acaba limitando as agências dos letramentos para o sistema escrito

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Data mining to estimate broiler mortality when exposed to heat wave Mineração de dados e estimativa da mortalidade alta de frangos quando expostos a onda de calor

Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.<br>As ondas de calor provocam perdas na produção animal pela sua exposição ao estresse por calor aumentando a mortalidade, e consequentemente, perdas econômicas. Bancos de dados zootécnicos e meteorológicos históricos podem conter informações que permitem modelar a mortalidade de frangos devido à incidência de ondas de calor. O objetivo foi analisar bancos de dados de frangos de corte associados a dados meteorológicos utilizando técnicas de mineração de dados, seleção de atributos e classificação (árvore de decisão) para modelar o impacto da incidência de onda de calor na mortalidade de frangos de corte. O Índice de Temperatura e Umidade (ITU) foi utilizado para descrever parte dos dados ambientais. A técnica de Mineração de Dados permitiu a construção de três modelos compreensíveis para estimar a alta mortalidade em frangos de corte. Os modelos gerados pela abordagem de seleção de atributos por Análise dos Componentes Principais e Wrapper apresentaram igual desempenho com uma precisão total de 89,3% e a classificação para alta mortalidade foi de 83,3%. Quando a seleção foi feita por especialistas do domínio, a precisão do modelo foi de 85,7%, e a da classificação para alta mortalidade foi de 76,9%. Resultados meteorológicos e o ITU calculada a partir de estações meteorológicas permitiram identificar condições ambientais prejudiciais para frangos entre 29 e 42 dias de vida. A técnica de Mineração de Dados é aplicável para construir modelos preditivos para a produção animal