{"id":"W2030423956","doi":"10.2135/cropsci2003.5490","title":"Prediction of Cultivar Performance Based on Single‐ versus Multiple‐Year Tests in Soybean","year":2003,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cultivar; Best linear unbiased prediction; Biology; Selection (genetic algorithm); Statistic; Crop; Predictive power; Statistics; Generalized linear mixed model; Genotype; Biotechnology; Agronomy; Mathematics; Computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003841281,0.00006519081,0.00006886631,0.00002867061,0.0001390061,0.00003137782,0.0001902306,0.00003177255,0.00003914608],"category_scores_gemma":[0.000268876,0.00002762885,0.00001915368,0.000640329,0.0001561126,0.00008421809,0.00001551252,0.00005972679,0.000008790821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002798423,"about_ca_system_score_gemma":0.00001903522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003414651,"about_ca_topic_score_gemma":0.00006102755,"domain_scores_codex":[0.999177,0.00001885567,0.0001190607,0.0002113823,0.000265958,0.000207767],"domain_scores_gemma":[0.9996703,0.0001263553,0.00004716295,0.00004644202,0.00005330974,0.00005640577],"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":[0.00002857303,0.00006475893,0.1906145,0.000002919228,3.001545e-7,5.14056e-7,0.00003374745,0.0005865092,0.8040313,0.00006313346,0.00001180366,0.004561961],"study_design_scores_gemma":[0.0003021064,0.0006273118,0.8484662,0.00004205965,0.000001382673,8.189046e-7,0.0000923821,0.01254121,0.1372076,0.00001182917,0.0006263047,0.00008084524],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994526,0.000007004731,0.000002655518,0.00004049335,0.0002115101,0.0000739317,0.00001560489,0.00001160238,0.005111242],"genre_scores_gemma":[0.9996663,0.000003701386,0.0002627865,0.00001905836,0.00002416454,0.000001826745,0.000003607496,3.46267e-7,0.0000181595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6668237,"threshold_uncertainty_score":0.1126672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07109522179588554,"score_gpt":0.2177811152648996,"score_spread":0.146685893469014,"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."}}