يعرض 1 - 20 نتائج من 114 نتيجة بحث عن '"Direct and indirect effect"', وقت الاستعلام: 0.86s تنقيح النتائج
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    المساهمون: Zugna, D, Popovic, M, Fasanelli, F, Heude, B, Scelo, G, Richiardi, L

    Relation: info:eu-repo/semantics/altIdentifier/pmid/36424556; info:eu-repo/semantics/altIdentifier/wos/WOS:000887904100001; volume:22; issue:1; firstpage:301; lastpage:313; numberofpages:13; journal:BMC MEDICAL RESEARCH METHODOLOGY; https://hdl.handle.net/2318/1880585; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85142375562

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    المؤلفون: Betti G., Neri L., Lonzi M., Lemmi A.

    المساهمون: Betti, G., Neri, L., Lonzi, M., Lemmi, A.

    وصف الملف: ELETTRONICO

    Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000523751400124; volume:12; issue:6; firstpage:2277; journal:SUSTAINABILITY; http://hdl.handle.net/11365/1119256; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85082803578; https://www.mdpi.com/2071-1050/12/6/2277

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    المصدر: Agronomía Colombiana; Vol. 38 Núm. 1 (2020); 3-8 ; Agronomía Colombiana; Vol. 38 No. 1 (2020); 3-8 ; Agronomía Colombiana; v. 38 n. 1 (2020); 3-8 ; 2357-3732 ; 0120-9965

    وصف الملف: application/pdf

    Relation: https://revistas.unal.edu.co/index.php/agrocol/article/view/80462/74913; Aliferis, C.F., A. Statnikov, I. Tsamardinos, S. Mani, and X.D. Koutsoukos. 2010. Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation. J. Mach. Learn. Res. 11(2010), 171-234.; Amaral, C.B., G.H.F. Oliveira, and G.V. Môro. 2019. Bayesian network: a simplified approach for environmental similarity studies in maize. Crop Breed. Appl. Biot. 19(1), 70-76. Doi:10.1590/1984-70332019v19n1a10; Cabral, P.D.S., A.T. de Amaral Junior, I.L. de J. Freitas, R.M. Ribeiro, and T.R. da C. Silva. 2016. Relação de causa e efeito de caracteres quantitativos sobre a capacidade de expansão do grão em milho-pipoca. Rev. Ciênc. Agron. 47(1), 108-117. Doi:10.5935/1806-6690.20160013; Carpentieri-Pípolo, V., H.W. Takahashi, R.M. Endo, M.R. Petek, and A.L. Seifert. 2002. Correlações entre caracteres quantitativos em milho pipoca. Hortic. Bras. 20(4), 551-554. Doi:10.1590/ S0102-05362002000400008; Coimbra, J.L.M., G. Benin, E.A. Vieira, A.C. de Oliveira, F.I.F. Carvalho, A.F. Guidolin, and A.P. Soares. 2005. Conseqüências da multicolinearidade sobre a análise de trilha em canola. Ciênc. Rural 35(2), 347-352. Doi:10.1590/S0103-84782005000200015; Cruz, C.D. 2013. Genes: a software package for analysis in experimental statistics and quantitative genetics. Acta Scient. Agron. 35(3), 271-276. Doi:10.4025/actasciagron.v35i3.21251; Cruz, C.D. and A.J. Regazzi. 1997. Modelos biométricos aplicados ao melhoramento genético. UFV, Viçosa, Brazil.; Cruz, C.D., A.J. Regazzi, and P.C.S. Carneiro. 2012. Modelos biométricos aplicados ao melhoramento genético. UFV, Viçosa, Brazil.; Cruz, J.C. (ed.). 2010. Cultivo do milho. Embrapa Milho e Sorgo. (Sistema de produção 1). Sete Lagoas - MG. URL: https://www.spo.cnptia.embrapa.br/listasptema?p_p_id=listaspportemaportlet_WAR_sistemasdeproducaolf6_1ga1ceportlet&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1p_r_p_619796851_temaId=1712&_listaspportemaportlet_WAR_sistemasdeproducaolf6_1ga1ceportlet_redirect=%2Ftemas-publicados (accessed 16 March 2020).; Daros, M., A.T. do Amaral Júnior, M.G. Pereira, F.S. Santos, C.A. Scapim, S. de P. Freitas Júnior, R.F. Daher, and M.R. Ávila. 2004. Correlations among agronomic traits in two recurrent selection cycles in popcorn. Ciênc. Rural 34(5), 1389-1394. Doi:10.1590/S0103-84782004000500010; Embrapa. 2006. Sistema brasileiro de classificação de solos. Embrapa, Brasilia.; Felipe, V.P., M.A. Silva, B.D. Valente, and G.J. Rosa. 2015. Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes. Poult. Scien. 94(4), 772-780. Doi:10.3382/ps/pev031; Lyerly, P.J. 1942. Some genetic and morphologic characters affecting the popping expansion of popcorn. J. Amer. Soc. Agron. 34, 986-995. Doi:10.2134/agronj1942.00021962003400110003x; Margaritis, D. 2003. Learning Bayesian network model structure from data. PhD thesis, Carnegie Mellon University, Pittsburgh, USA.; Marques, R.L. and I. Dutra, 2002. Redes Bayesianas: o que são, para que servem, algoritmos e exemplos de aplicações. Coppe Sistemas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.; Mohsenin, N.N. 1970. Physical properties of plant and animal materials: structure, physical characteristics and mechanical properties. Gordon and Breach, New York, USA. Doi:10.1002/food.19870310724; Pearl, J. 2000. Causality: models, reasoning and inference. Cambridge University Press, New York, USA. Doi:10.1017/S0266466603004109; Pordesimo, L.O., R.C. Anantheswaran, A.M. Fleiscaman, Y.E. Lin, and M.A. Hanna. 1990. Physical properties as indicators of popping characteristics of microwave popcorn. J. Food Sci. 55(5), 1352-1355. Doi:10.1111/j.1365-2621.1990.tb03934.x; Ribeiro, R.M., A.T. do Amaral Júnior, G.F. Pena, M. Vivas, R.N. Kurosawa, and L.S.A. Gonçalves. 2016. Effect of recurrent selection on the variability of the UENF-14 popcorn population. Crop Breed. Appl. Biotechnol. 16(2), 123-131. Doi:10.1590/1984-70332016v16n2a19; Scutari, M. 2009. Learning Bayesian networks with the bnlearn R package. CRC Press, Florida, USA. Doi:10.18637/jss.v035.i03; Souza, T.V. 2003. Aspectos estatísticos da análise de trilha (Path analysis) aplicada em experimentos agrícolas. MSc thesis, Universidade Federal de Lavras, Lavras, Brazil.; Soylu, S. and A. Tekkanat. 2006. Interactions amongst kernel properties and expansion volume in various popcorn genotypes. J. Food Eng. 80(1), 336-341. Doi:10.1016/j.jfoodeng.2006.06.001; Vencovsky, R. and P. Barriga. 1992. Genética biométrica no fitomelhoramento. Sociedade Brasileira de Genética, Ribeirão Preto, Brazil.; Yu, J., V.A. Smith, P.P. Wang, A.J. Hartemink, and E.D. Jarvis. 2004. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18), 3594-3603. Doi:10.1093/bioinformatics/bth448; https://revistas.unal.edu.co/index.php/agrocol/article/view/80462

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