{"id":"W2909553073","doi":"10.5539/jas.v11n2p100","title":"Methods of Soybean Genotypes Selection in Paraná State, Brazil","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Estadual de Maringá; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Cultivar; Adaptability; Crop; Agronomy; Biology; Growing season; Mathematics; Selection (genetic algorithm); Horticulture; Gene–environment interaction; Genotype; Ecology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009188924,0.0009282999,0.0007420838,0.002063965,0.000863686,0.0004464473,0.0007773782,0.0002485607,0.002895063],"category_scores_gemma":[0.0007947501,0.0004103207,0.0005478786,0.001431147,0.0003892091,0.0002340809,0.0005347143,0.0005915051,0.001125519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008329105,"about_ca_system_score_gemma":0.001237275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0177869,"about_ca_topic_score_gemma":0.04549337,"domain_scores_codex":[0.9986175,0.0002212385,0.000128092,0.0006233845,0.0003251916,0.00008448783],"domain_scores_gemma":[0.9995582,0.0000570689,0.000108665,0.00008011493,0.0001502238,0.00004567262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009988076,0.001528834,0.1654468,0.001287056,0.0001662409,0.002298256,0.006492539,0.001843132,0.5802902,0.003429807,0.002932022,0.2332864],"study_design_scores_gemma":[0.0002607773,0.002472338,0.7964455,0.0003650542,0.0004303129,0.001818532,0.003550424,0.004949439,0.07716051,0.002137032,0.1101207,0.000289331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8083348,0.002491695,0.1284287,0.0002843354,0.0001153383,0.01190566,0.01474153,0.001022155,0.03267578],"genre_scores_gemma":[0.6470503,0.002203693,0.2971178,0.000407469,0.00002306418,0.01775813,0.01364474,0.0004108027,0.02138402],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0177869,"threshold_uncertainty_score":0.03536671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454908510652618,"score_gpt":0.2760149053472715,"score_spread":0.2614658202407453,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}