{"id":"W2118312111","doi":"10.1556/crc.37.2009.1.13","title":"Exploring associations between agronomic and chemical composition traits for barley improvement","year":2009,"lang":"en","type":"article","venue":"Cereal Research Communications","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Biplot; Hordeum vulgare; Test weight; Agronomy; Biology; Cultivar; Trait; Starch; Selection (genetic algorithm); Chemical composition; Composition (language); Biotechnology; Poaceae; Genotype; Food science; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121894,0.00005653833,0.00009157291,0.00001968974,0.000650006,0.00009928527,0.0003810602,0.00004217966,0.000007038245],"category_scores_gemma":[0.0000747164,0.0000303308,0.00003466812,0.000145005,0.00005240382,0.00009307102,0.0001300826,0.0001707652,0.000003973316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005967284,"about_ca_system_score_gemma":0.00001002683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001596392,"about_ca_topic_score_gemma":0.0001146685,"domain_scores_codex":[0.9992551,0.00006706123,0.000151314,0.0001389073,0.0001455744,0.000242009],"domain_scores_gemma":[0.9986542,0.000976881,0.00003378494,0.00009519522,0.0001325753,0.0001072898],"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":[0.000004640648,0.00005264208,0.00092113,0.000001743461,0.000008512781,2.925509e-8,0.0001232995,5.414333e-7,0.6420371,0.003387233,0.000256446,0.3532067],"study_design_scores_gemma":[0.0001825334,0.0002967638,0.9802601,0.00002217062,0.00001233233,5.437082e-7,0.0002386464,0.0003046947,0.008914238,0.004850187,0.004788897,0.0001289489],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988639,0.00009504418,0.00001458516,0.009360701,0.000008519131,0.0003249236,0.0002722239,0.0000217973,0.001263238],"genre_scores_gemma":[0.9978237,0.0004254789,0.0008489461,0.00003308255,0.0001264207,0.00007335607,0.0006493318,6.500172e-7,0.00001905337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9793389,"threshold_uncertainty_score":0.4999386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5000569565963662,"score_gpt":0.3744764934392669,"score_spread":0.1255804631570993,"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."}}