{"id":"W2910127722","doi":"10.5539/jas.v11n2p109","title":"Phenolic Composition and Allelopathy of Libidibia ferrea Mart. ex Tul. in Weeds","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Allelopathy and phytotoxic interactions","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico","keywords":"Allelopathy; Calotropis procera; Bark (sound); Phytochemical; Botany; Biology; Caffeic acid; Weed; Horticulture; Germination; Chemistry; Antioxidant","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":[0.00007033015,0.0002964208,0.0002145256,0.0007740072,0.0003440108,0.0001795059,0.0000665149,0.0001639669,0.00081668],"category_scores_gemma":[0.00009494514,0.00009207227,0.0001613017,0.0001926591,0.0001100851,0.0001478359,0.0001253558,0.0002726611,0.00008088483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001694271,"about_ca_system_score_gemma":0.0001151787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002676074,"about_ca_topic_score_gemma":0.005086358,"domain_scores_codex":[0.9999727,0.000003223063,0.000001808505,0.000006553725,0.00000742068,0.000008284313],"domain_scores_gemma":[0.999956,0.000005930418,0.00001279963,0.000001952998,0.000008004167,0.00001533581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002968809,0.0001240119,0.00462536,0.0001538303,0.00001655712,0.0001041263,0.0001301293,0.00004780606,0.9867312,0.00003152885,0.00004907707,0.007689456],"study_design_scores_gemma":[0.00007032608,0.00223638,0.805956,0.00006951598,0.0001371843,0.0007865546,0.0005137488,0.0007236392,0.1832844,0.0001616673,0.0060311,0.00002954857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980633,0.001104229,0.0000962936,0.00003507623,0.000004504596,0.000007723617,0.00009922965,0.00001164275,0.0005779751],"genre_scores_gemma":[0.9970299,0.0005712989,0.0004094522,0.00006460013,0.000007457725,0.00001251699,0.0003198904,0.000004522683,0.001580332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002676074,"threshold_uncertainty_score":0.005320966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009580945222008296,"score_gpt":0.2148236209009958,"score_spread":0.2052426756789875,"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."}}