{"id":"W2999009349","doi":"10.1002/csc2.20035","title":"Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds","year":2020,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agricultural Research Service; National Institute of Food and Agriculture; Nebraska Corn Board; National Science Foundation; Iowa State University; University of Wisconsin-Madison; U.S. Department of Agriculture","keywords":"Biology; Trait; Gene–environment interaction; Quantitative trait locus; Phenology; Variance components; Agronomy; Sowing; Genotype; Growing season; Phenotypic trait; Yield (engineering); Phenotype; Genetics; Statistics; Gene","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":[0.0003125179,0.00007149261,0.000123326,0.000008101378,0.0001285086,0.00005428018,0.0001691582,0.00005082149,0.0001036224],"category_scores_gemma":[0.0001017636,0.00003040158,0.00002329567,0.0001657092,0.000332193,0.0001688364,0.00009072196,0.0000825804,0.000002303458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000165976,"about_ca_system_score_gemma":0.000007343962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003100062,"about_ca_topic_score_gemma":0.000005806737,"domain_scores_codex":[0.9992153,0.000025169,0.0001674361,0.0003170741,0.0001014351,0.0001735555],"domain_scores_gemma":[0.9996679,0.0001390167,0.00007881067,0.00001899278,0.00001712214,0.00007818454],"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":[0.0000248675,0.00002750877,0.005159432,0.000002965778,9.068694e-7,6.357987e-7,0.0001326642,0.00001758082,0.9563875,0.00006574113,0.00004062803,0.03813957],"study_design_scores_gemma":[0.0001737681,0.0004338215,0.9678622,0.00001278067,0.000005519059,0.000005012485,0.0005961264,0.007481606,0.02047864,0.0007402888,0.002065856,0.0001443095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981154,0.0001113734,0.0003062865,0.0009622909,0.00002830781,0.0001447269,0.00002820951,0.000007630042,0.0002957954],"genre_scores_gemma":[0.9986672,0.00003128972,0.001160434,0.0001024284,0.0000223896,0.000004546799,0.000005559036,3.102273e-7,0.000005785138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9627029,"threshold_uncertainty_score":0.123974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0803872867027248,"score_gpt":0.2522048701880948,"score_spread":0.17181758348537,"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."}}