{"id":"W2953610201","doi":"10.5539/jas.v11n11p81","title":"Genetic Divergence in Urena lobata Accessions to Quantitative Traits","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"UPGMA; Germplasm; Biology; Dendrogram; Genetic divergence; Heritability; Selection (genetic algorithm); Divergence (linguistics); Genetic variation; Horticulture; Genetic diversity; Evolutionary biology; Genetics; Computer science; Population; Gene; Demography; Artificial intelligence","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":[0.0004146996,0.0001907395,0.0003198332,0.001312633,0.0002748896,0.000353124,0.0001591844,0.0002037822,0.001286822],"category_scores_gemma":[0.0006200032,0.0001096522,0.0002306567,0.0006095829,0.0001920432,0.0001201097,0.0003648684,0.0002410376,0.0002050505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001656994,"about_ca_system_score_gemma":0.00009732028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005748898,"about_ca_topic_score_gemma":0.001503645,"domain_scores_codex":[0.9996629,0.00009018349,0.00003712482,0.0001306539,0.00004937677,0.0000298014],"domain_scores_gemma":[0.9995674,0.0001946266,0.00008005244,0.0000621176,0.00004337975,0.00005235787],"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":[0.0009243009,0.0001826008,0.2944993,0.0001084742,0.0002902058,0.0006074792,0.002007774,0.0003924338,0.6765379,0.0004431339,0.00006810309,0.02393824],"study_design_scores_gemma":[0.000017713,0.0001318777,0.9924473,0.00001265005,0.00004442148,0.0005381249,0.0002815383,0.0004205575,0.005122911,0.0001590879,0.0008094962,0.00001438937],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992092,0.00007676326,0.0003520662,0.000005000383,0.000001561291,0.000003765347,0.00007734555,0.000006830379,0.0002675073],"genre_scores_gemma":[0.9987337,0.0000421741,0.0004445447,0.00001121838,0.000002696976,0.00001243088,0.0004095604,0.000009287826,0.0003343684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001312633,"threshold_uncertainty_score":0.004304886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02171541150620553,"score_gpt":0.266125380714399,"score_spread":0.2444099692081935,"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."}}