{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007318733,0.0004015132,0.0004110278,0.0008405994,0.0003343487,0.0006223717,0.0003812748,0.0004308663,0.002352532],"category_scores_gemma":[0.0007694305,0.0002424218,0.000320831,0.001205661,0.0002279081,0.0003912443,0.0002579112,0.0006766123,0.0002378248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006082794,"about_ca_system_score_gemma":0.0007410912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003542418,"about_ca_topic_score_gemma":0.006122799,"domain_scores_codex":[0.9997485,0.00009240369,0.00001561984,0.00006484736,0.00004169403,0.00003691883],"domain_scores_gemma":[0.9992542,0.0003707881,0.0001521424,0.0000292435,0.00005713371,0.0001364685],"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":[0.002121238,0.0005124425,0.08133182,0.0001218092,0.000159568,0.0002971225,0.000118361,0.000806114,0.8875489,0.0003999227,0.0001115423,0.02647112],"study_design_scores_gemma":[0.0001732455,0.001404875,0.9069589,0.00001946537,0.0003862956,0.0003537141,0.0002885576,0.004003904,0.0835204,0.0006876665,0.002164176,0.00003874989],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972605,0.0002885574,0.001380727,0.0001006787,0.000007480579,0.000017466,0.0002204931,0.00002535948,0.0006987444],"genre_scores_gemma":[0.9941064,0.0003134677,0.004141329,0.00006546622,0.000008605221,0.00001270518,0.0002990018,0.00002303902,0.001029934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003542418,"threshold_uncertainty_score":0.007870018,"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."}}