{"id":"W2103261503","doi":"10.5539/jas.v4n3p247","title":"Improving Genetic Attributes of Confectionary Traits in Peanut (Arachis hypogaea L.) Using Multivariate Analytical Tools","year":2011,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Peanut Plant Research Studies","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arachis hypogaea; Cultivar; UPGMA; Point of delivery; Multivariate statistics; Yield (engineering); Biology; Biotechnology; Multivariate analysis; Horticulture; Food science; Agronomy; Mathematics; Genetic variation; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081381,0.0004375012,0.0002184115,0.001224899,0.0001934375,0.0004458608,0.0001512616,0.0001482938,0.0004930393],"category_scores_gemma":[0.0008499961,0.0001211537,0.0004061592,0.0008755132,0.0001816223,0.0002561534,0.0002535097,0.0004026491,0.00008539795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002800054,"about_ca_system_score_gemma":0.0002146967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002857197,"about_ca_topic_score_gemma":0.004761132,"domain_scores_codex":[0.9997596,0.00007409274,0.00002073662,0.00007082868,0.00005262361,0.00002208389],"domain_scores_gemma":[0.9993936,0.0001745263,0.0002259857,0.0000483287,0.00009335532,0.00006409991],"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":[0.000555602,0.0002701464,0.2225775,0.0001458527,0.0002367329,0.0003135117,0.0005929783,0.002803152,0.6963747,0.0002848512,0.0001359262,0.07570908],"study_design_scores_gemma":[0.000006104287,0.0002199387,0.9675729,0.00001044057,0.00007419464,0.0002982924,0.00023953,0.008156876,0.02269879,0.000212633,0.00047375,0.00003655792],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942205,0.0001146064,0.005148786,0.0000241525,0.000002386442,0.000008289559,0.0002128072,0.0000348265,0.0002335566],"genre_scores_gemma":[0.9859098,0.0001420947,0.01275884,0.00001460683,0.000003774101,0.00001578096,0.0007158578,0.00002193018,0.0004173674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002857197,"threshold_uncertainty_score":0.005681157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0970702987413016,"score_gpt":0.275275397782847,"score_spread":0.1782050990415454,"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."}}