{"id":"W2003823047","doi":"10.1300/j411v14n01_12","title":"QTL Identification, Mega-Environment Classification, and Strategy Development for Marker-Based Selection Using Biplots","year":2005,"lang":"en","type":"article","venue":"Journal of Crop Improvement","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"","keywords":"Quantitative trait locus; Biplot; Family-based QTL mapping; Marker-assisted selection; Biology; Selection (genetic algorithm); Trait; Genetic marker; Genetics; Computer science; Artificial intelligence; Gene mapping; Genotype; Chromosome; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003399928,0.001026064,0.001236964,0.002734307,0.0003298887,0.001177611,0.0007990531,0.000210908,0.004189944],"category_scores_gemma":[0.005715514,0.0006087103,0.0008052211,0.002366175,0.0002449287,0.0006224852,0.0006132208,0.0007853482,0.001725434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005027899,"about_ca_system_score_gemma":0.0005206269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002069188,"about_ca_topic_score_gemma":0.001847742,"domain_scores_codex":[0.9976587,0.001346942,0.0001641013,0.0003640711,0.0003536565,0.000112456],"domain_scores_gemma":[0.9975783,0.001291879,0.0002257898,0.0002970386,0.0004804783,0.0001264546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001758382,0.0003276862,0.01138626,0.0007260233,0.0004112321,0.0004085074,0.0004292665,0.02733996,0.1263397,0.00747548,0.007716293,0.8156812],"study_design_scores_gemma":[0.0007461637,0.001357052,0.07843228,0.0002314423,0.0004534708,0.001365474,0.0003463648,0.6954377,0.1198383,0.02122298,0.08015935,0.0004092944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0163332,0.0001839261,0.9772437,0.00006904428,0.00002990954,0.0001800114,0.0006305458,0.004567389,0.0007622864],"genre_scores_gemma":[0.04884005,0.00010993,0.9477202,0.00004890556,0.00001355845,0.0003476419,0.001365891,0.0008000378,0.0007538023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004189944,"threshold_uncertainty_score":0.01798075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292458797024502,"score_gpt":0.2608383339074957,"score_spread":0.2315924542050455,"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."}}