{"id":"W2130505531","doi":"10.3389/fgene.2014.00227","title":"Analysis of linear and non-linear genotype × environment interaction","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture Food and Rural Development; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gene–environment interaction; Interaction; Statistics; Cauchy distribution; Quantitative trait locus; Range (aeronautics); Linear model; Mathematics; Linear regression; Function (biology); Genotype; Nonlinear system; Biology; Genetics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001294909,0.00006379389,0.0001513288,0.00003689252,0.00003123908,0.000006840821,0.0000765981,0.00005479841,0.0000320681],"category_scores_gemma":[0.000008030013,0.00003066575,0.0000378575,0.0001866633,0.00002498494,0.000011151,0.00003627008,0.00004914645,0.000001712604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008119866,"about_ca_system_score_gemma":0.000001044436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004878681,"about_ca_topic_score_gemma":0.00009787187,"domain_scores_codex":[0.9994953,0.00002319251,0.0001494782,0.0001416841,0.00008744343,0.0001029163],"domain_scores_gemma":[0.9998246,0.00003042708,0.00005852933,0.0000385857,0.00001051351,0.00003738609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001809422,0.00003414826,0.8349519,0.000005350049,0.0000836832,4.221702e-7,0.0001527526,0.01189085,0.0430992,0.000003678171,0.0001567072,0.1096032],"study_design_scores_gemma":[0.00007253882,0.0001456242,0.6923347,0.000005341999,0.0001094031,3.701961e-7,0.000162811,0.29292,0.002495642,0.00006213902,0.01159417,0.00009726085],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974919,0.000250095,0.001824764,0.00005788949,0.0001500623,0.00005061238,0.00001581991,0.000002917332,0.0001559504],"genre_scores_gemma":[0.9915739,0.0007052384,0.007546457,0.00002959692,0.00007004348,0.000001189717,0.00003988769,5.345975e-7,0.00003318532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2810292,"threshold_uncertainty_score":0.1250513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454175388660455,"score_gpt":0.1981207808336345,"score_spread":0.1835790269470299,"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."}}