{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006588383,0.0006800406,0.0007811078,0.0006194495,0.0002384551,0.0007901129,0.0005628394,0.0005730057,0.0003286967],"category_scores_gemma":[0.009337148,0.0003011884,0.0008808018,0.0004676727,0.000372871,0.000649823,0.0003877298,0.0006883221,0.000194384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001925752,"about_ca_system_score_gemma":0.0008630304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02219878,"about_ca_topic_score_gemma":0.05291506,"domain_scores_codex":[0.9977139,0.001161413,0.0001408757,0.000475833,0.0003714041,0.0001365371],"domain_scores_gemma":[0.9867219,0.009845091,0.001530015,0.0006872791,0.000577469,0.0006383632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008113762,0.0005279321,0.7427247,0.000187121,0.001039685,0.0002987542,0.0006540868,0.0514222,0.1409425,0.0002767714,0.0005492107,0.05326331],"study_design_scores_gemma":[0.00006002671,0.004743071,0.9056309,0.00002065232,0.0002088129,0.0001418769,0.0001436851,0.06821919,0.01999608,0.0002874421,0.0004444571,0.0001038814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957416,0.0001801927,0.003381369,0.00002005941,0.000005488263,0.00001297648,0.000349728,0.00005674229,0.0002519229],"genre_scores_gemma":[0.9953311,0.00006516375,0.003243885,0.00001724497,0.000002655585,0.00001968018,0.0008421183,0.00002729751,0.0004508274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02219878,"threshold_uncertainty_score":0.04413915,"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."}}