{"id":"W2088355025","doi":"10.2135/cropsci2007.05.0254","title":"Breeding Line Selection Based on Multiple Traits","year":2008,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":191,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Biplot; Selection (genetic algorithm); Trait; Biology; Avena; Biotechnology; Culling; Breeding program; Quantitative trait locus; Genomic selection; Index selection; Genotype; Genetics; Computer science; Agronomy; Machine learning; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002440235,0.00007228176,0.00006197536,0.00002253313,0.0007722623,0.00005895575,0.0002352516,0.00003160779,0.00009232653],"category_scores_gemma":[0.0001500017,0.00002789002,0.00002852786,0.0007413216,0.0001552493,0.0000835186,0.00002185743,0.0000724862,0.00002949562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001874129,"about_ca_system_score_gemma":0.00002035996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007443716,"about_ca_topic_score_gemma":0.00007959544,"domain_scores_codex":[0.9990894,0.000009178523,0.00008745141,0.0002592793,0.0003010138,0.0002536802],"domain_scores_gemma":[0.9996531,0.0001194253,0.00003380791,0.00002078392,0.00007320899,0.00009967314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006763483,0.00002445015,0.01797066,6.918616e-7,2.594664e-7,0.000001905523,0.00002646521,0.0008361886,0.9663936,0.00002455288,0.0001432279,0.01457126],"study_design_scores_gemma":[0.0001177638,0.0005254682,0.7873482,0.00001698585,0.000001705353,0.00002865406,0.00002655148,0.1048041,0.1025822,0.00002751291,0.004350737,0.0001700225],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974447,0.000008774365,0.00004617319,0.0003799948,0.0001280908,0.00006236845,0.000007832058,0.0000449119,0.001877145],"genre_scores_gemma":[0.9990667,0.000005327046,0.0003474224,0.0002500697,0.0001894109,0.000002201948,0.000003361217,3.610341e-7,0.0001351151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8638113,"threshold_uncertainty_score":0.5939696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05985369785916165,"score_gpt":0.2173344272803902,"score_spread":0.1574807294212285,"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."}}