{"id":"W2895436720","doi":"10.5539/jas.v10n11p572","title":"Artificial Neural Network and Multivariate Models Applied to Morphological Traits and Seeds of Common Beans Genotypes","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Biology; Multivariate statistics; Cultivar; Genotype; Selection (genetic algorithm); Biotechnology; Statistics; Artificial intelligence; Mathematics; Agronomy; Computer science; Genetics","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":[0.001000196,0.0005367612,0.0003621325,0.0005999929,0.000203369,0.0006621283,0.0004414328,0.0004341321,0.0009769966],"category_scores_gemma":[0.002078041,0.0002323044,0.00069331,0.0004270693,0.0002334624,0.000401398,0.0003946998,0.0005942052,0.0001524373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006288829,"about_ca_system_score_gemma":0.0004938545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00827802,"about_ca_topic_score_gemma":0.006332476,"domain_scores_codex":[0.9996326,0.0001697844,0.00002355103,0.00008911865,0.00004068908,0.00004422625],"domain_scores_gemma":[0.9993308,0.0003827835,0.00009538562,0.00004465176,0.0001170754,0.00002926748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000283496,0.0001798956,0.01913874,0.00005558494,0.0001922992,0.00005253795,0.00005619716,0.9155167,0.002335657,0.001697627,0.0005031232,0.0599881],"study_design_scores_gemma":[0.000001612788,0.00001721308,0.002085159,0.000002207996,0.000005137105,0.000004540047,0.000005200674,0.9973534,0.0001431987,0.0003318847,0.00004776685,0.000002645591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6556759,0.0005394628,0.3408261,0.0003399044,0.00007054643,0.0000556897,0.0003649735,0.0002801917,0.001847067],"genre_scores_gemma":[0.9814401,0.00009752205,0.01678171,0.00001757885,0.00001393919,0.00004557048,0.0002345567,0.00001306191,0.001356049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00827802,"threshold_uncertainty_score":0.01645964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04066567192860299,"score_gpt":0.227899755551987,"score_spread":0.187234083623384,"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."}}