{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004662442,0.0001009763,0.00021107,0.00004062964,0.0001004318,0.00005879248,0.0002948426,0.00006334775,0.0001416921],"category_scores_gemma":[0.00002841126,0.00003339197,0.00007267341,0.0006944056,0.000127884,0.0007240206,0.00006629883,0.0002460351,0.00001261279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005224577,"about_ca_system_score_gemma":0.00002125298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006080124,"about_ca_topic_score_gemma":0.00008023701,"domain_scores_codex":[0.9988526,0.0000466188,0.0003737206,0.0001538438,0.0003671646,0.0002060223],"domain_scores_gemma":[0.9992183,0.00009837515,0.0003198844,0.00003236905,0.0002285063,0.0001025544],"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":[0.00002613332,0.00007552112,0.01395747,0.000004555512,0.000003466727,0.00000223966,0.0004125394,0.00003100755,0.9823157,0.0001971002,0.00009833866,0.00287596],"study_design_scores_gemma":[0.0001605307,0.0003699034,0.9842555,0.00009484738,0.000005275947,0.0002013852,0.001319617,0.000020813,0.01282089,0.0001267801,0.0005219075,0.0001025504],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966677,0.0001064119,7.772657e-7,0.00101624,0.0002572053,0.0001097764,0.000004299887,0.00000483031,0.001832758],"genre_scores_gemma":[0.9994674,0.00007403947,0.0001196098,0.00006969219,0.0001228873,7.277115e-7,0.000002208228,3.595945e-7,0.0001431161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9702981,"threshold_uncertainty_score":0.1551429,"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."}}