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| Registros recuperados : 9 | |
| 6. |  | ARTIAGA, O. P.; SPECHAR, C. R.; SILVA, P. P.; NASCIMENTO, W. M. Genótipos de gráo de bico para cultivo na região geoeconômico do Distrito Federal. Horticultura Brasileira, Brasília, DF, v. 29, n. 2, p. 4265-4271, jul. 2011. CD-ROM. Suplemento. Trabalho apresentado no 51. Congresso Brasileiro de Olericultura, Viçosa, MG.| Biblioteca(s): Embrapa Hortaliças. |
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| 7. | | ARTIAGA, O. P.; SPEHAR, C. R.; SILVA, P. P.; NASCIMENTO, W. M.; BOITEUX, L. S. Chemical and genetic control of the Fusarium species complex in chickpea and their effects on stand stablishment under highland subtropical conditions. Acta Horticulturae, v. 1249, p. 189-192, Sept. 2019. Edição do Proceedings of VI International Symposium on Seed, Transplant and Stand Establishment of Horticultural Crops, Brasília.| Biblioteca(s): Embrapa Hortaliças. |
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| 8. | | ARTIAGA, O. P.; SPEHAR, C. R.; SILVA, P. P.; NASCIMENTO, W. M.; BOITEUX, L. S. Chemical and genetic control of fusarium wilt in chickpea and their effects on stand establishment under tropical conditions. In: INTERNATIONAL SYMPOSIUM ON SEED, TRANSPLANT AND STAND ESTABLISHMENT OS HORTICULTURAL CROPS: SOWING THE FUTURE OF TROPICAL HORTICULTURE, 6., 2012, Brasília, DF. Abstracts... Brasília, DF: Embrapa, 2012. p. 100. Abstract 87.| Biblioteca(s): Embrapa Hortaliças. |
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| 9. | | PINHEIRO, J. B.; BERNARDES NETO, J. F.; BISCAIA, D.; ARTIAGA, O. P.; SILVA, G. O. da; MACÊDO, A. G.; NASCIMENTO, W. M.; SUINAGA, F. A.; SILVA, P. P. Avaliação de genótipos de grão-de-bico para resistência ao nematóides-das-galhas. In: CONGRESSO BRASILEIRO DE NEMATOLOGIA, 34., 2017, Vitória. Nematoides: manejo, desafios e soluções. Piracicaba: Sociedade Brasileira de Nematologia, 2017.| Biblioteca(s): Embrapa Hortaliças. |
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| Registros recuperados : 9 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
13/10/2010 |
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Data da última atualização: |
05/11/2010 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
GARGIULO, F.; TERNES, S.; HUET, S.; DEFFUANT, G. |
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Afiliação: |
FLORIANA GARGIULO, LISC/CEMAGREF; SONIA TERNES, CNPTIA, LISC/CEMAGREF; SYLVIE HUET, LISC/CEMAGREF; GUILLAUME DEFFUANT, LISC/CEMAGREF. |
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Título: |
An iterative approach for generating statistically realistic populations of households. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
PLoS ONE, San Francisco, v. 5, n. 1, 2010. |
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Páginas: |
p. 1-9. |
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DOI: |
10.1371/journal.pone.0008828.t002 |
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Idioma: |
Inglês |
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Conteúdo: |
Background. Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings. This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance. We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time. MenosBackground. Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings. This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance. We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reason... Mostrar Tudo |
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Palavras-Chave: |
Algorithm; Algoritmo; Artificial population; População x tarefas do lar; Population distributed in households; Projeto PRIMA; Prototypical Policy Impacts on Multifunctional Activities - PRIMA; Simulation; Synthetic population. |
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Thesagro: |
Simulação. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/23288/1/journal.pone.0008828.pdf
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Marc: |
LEADER 02450naa a2200301 a 4500
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005 2010-11-05
008 2010 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pone.0008828.t002$2DOI
100 1 $aGARGIULO, F.
245 $aAn iterative approach for generating statistically realistic populations of households.$h[electronic resource]
260 $c2010
300 $ap. 1-9.
520 $aBackground. Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings. This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance. We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time.
650 $aSimulação
653 $aAlgorithm
653 $aAlgoritmo
653 $aArtificial population
653 $aPopulação x tarefas do lar
653 $aPopulation distributed in households
653 $aProjeto PRIMA
653 $aPrototypical Policy Impacts on Multifunctional Activities - PRIMA
653 $aSimulation
653 $aSynthetic population
700 1 $aTERNES, S.
700 1 $aHUET, S.
700 1 $aDEFFUANT, G.
773 $tPLoS ONE, San Francisco$gv. 5, n. 1, 2010.
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Embrapa Agricultura Digital (CNPTIA) |
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