{"id":"W2050054634","doi":"10.2135/cropsci2000.4011","title":"Genotype × Region Interaction for Two‐Row Barley Yield in Canada","year":2000,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada; Nova Scotia Department of Agriculture","funders":"","keywords":"Hordeum vulgare; Biology; Genotype; Selection (genetic algorithm); Yield (engineering); Subdivision; Adaptation (eye); Local adaptation; Grain yield; Poaceae; Variance (accounting); Breeding program; Agronomy; Geography; Cultivar; Demography; Genetics; Population","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005408708,0.0005776114,0.0006110895,0.0005979377,0.001015843,0.0008531327,0.0007578387,0.0003117034,0.002181562],"category_scores_gemma":[0.001085899,0.0002987791,0.0008716633,0.000967332,0.0006082829,0.0002223004,0.0005845694,0.0005588807,0.0002465909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009287464,"about_ca_system_score_gemma":0.01051489,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9351467,"about_ca_topic_score_gemma":0.971655,"domain_scores_codex":[0.999236,0.0001187249,0.00003433933,0.0002360053,0.0001841599,0.0001908859],"domain_scores_gemma":[0.9987478,0.000369019,0.0001429715,0.00008525104,0.0003639267,0.0002909582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00519364,0.0003037219,0.7526082,0.0001728723,0.001354885,0.001317505,0.00196713,0.005482342,0.190008,0.001274435,0.003174551,0.03714269],"study_design_scores_gemma":[0.00003840997,0.0001545398,0.9939908,0.00001152269,0.0001941135,0.00008004439,0.0003165068,0.001964988,0.00186261,0.00007291976,0.001274306,0.00003912024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974235,0.0002132808,0.000311717,0.00006115797,0.000007942962,0.00001073173,0.0009425199,0.00004058097,0.0009885596],"genre_scores_gemma":[0.9957962,0.0001222564,0.0006642925,0.0000480965,0.000001393782,0.00001496113,0.001041341,0.00004845325,0.002263098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06485331,"threshold_uncertainty_score":0.1304705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04413189995214815,"score_gpt":0.2203749875109376,"score_spread":0.1762430875587895,"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."}}