{"id":"W2024544257","doi":"10.2135/cropsci2003.2018","title":"Genetic Components of Yield Stability in Maize Breeding Populations","year":2003,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diallel cross; Biology; Agronomy; Selection (genetic algorithm); Trait; Population; Stability (learning theory); Grain yield; Additive genetic effects; Plant breeding; Genetic gain; Genetic variability; Genetic variation; Biotechnology; Genetics; Genotype; Heritability; Gene; Hybrid","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004524146,0.000197091,0.0002355806,0.0008770043,0.0002422643,0.0003271658,0.0002355047,0.0001752506,0.0002876521],"category_scores_gemma":[0.00103027,0.0001852847,0.0002179555,0.0005624078,0.0003052424,0.0001453121,0.000330176,0.0002349359,0.00005370882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004767183,"about_ca_system_score_gemma":0.0002227183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003224596,"about_ca_topic_score_gemma":0.003626975,"domain_scores_codex":[0.999709,0.00005268356,0.00002583237,0.0001061144,0.00007694394,0.00002935404],"domain_scores_gemma":[0.9995908,0.00009783627,0.0001524574,0.0000470365,0.00006263289,0.00004925695],"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":[0.0007429671,0.00005864171,0.216045,0.00003910816,0.0002412908,0.0001478688,0.0007529138,0.001661485,0.7633654,0.0005439013,0.00004520659,0.01635621],"study_design_scores_gemma":[0.00001536863,0.0002143338,0.9879798,0.000002650855,0.00006316131,0.0001204234,0.00006238994,0.001839158,0.009308458,0.0002009597,0.0001795099,0.00001379255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994311,0.00004968161,0.0003642344,0.000003767624,2.588222e-7,0.000003071431,0.00004063334,0.00000745336,0.00009985096],"genre_scores_gemma":[0.9991666,0.00003349931,0.0005163836,0.000003830631,0.000001431372,0.000005652749,0.000179598,0.000005614817,0.00008739803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003224596,"threshold_uncertainty_score":0.006411612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1455561451209413,"score_gpt":0.2498147712523067,"score_spread":0.1042586261313654,"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."}}