{"id":"W2895503883","doi":"10.5539/jas.v10n11p367","title":"Productive and Nutritional Aspects of Tithonia diversifolia Fertilized With Biofertilizer and Irrigated","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Tithonia; Biofertilizer; Sunflower; Irrigation; Dry matter; Agronomy; Leaf area index; Cutting; Chlorophyll; Horticulture; Mathematics; Biology","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":[0.0001981077,0.000326858,0.0002848686,0.0002667313,0.0001977287,0.0002279915,0.0001896982,0.0001659783,0.0003692574],"category_scores_gemma":[0.0001439318,0.0001113933,0.0002436983,0.0001762413,0.0001566964,0.000305076,0.0001775209,0.0002398464,0.00004530592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004065653,"about_ca_system_score_gemma":0.0002338685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003718911,"about_ca_topic_score_gemma":0.004582028,"domain_scores_codex":[0.9999232,0.00001072388,0.000007741702,0.00002823841,0.00001474989,0.00001523124],"domain_scores_gemma":[0.9998529,0.00002177767,0.00005440025,0.000009927072,0.00001815738,0.00004285942],"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":[0.0005964578,0.0001341063,0.005218126,0.0000940611,0.00002531569,0.00008333781,0.00008846434,0.0001277387,0.9896687,0.00003489989,0.0000206214,0.003908165],"study_design_scores_gemma":[0.00009417017,0.004968023,0.7665303,0.00002560048,0.0001683586,0.0002895242,0.00043341,0.002248861,0.2218298,0.0001453063,0.003227771,0.00003886242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995772,0.0001950525,0.00007134287,0.000008895489,0.000002658991,0.000003530362,0.00004106158,0.000002713312,0.00009764945],"genre_scores_gemma":[0.9985537,0.0002318876,0.0003974861,0.00003289706,0.000004146178,0.00001156201,0.0002258501,0.000005013107,0.0005374702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003718911,"threshold_uncertainty_score":0.007394552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817563171441542,"score_gpt":0.249014759451151,"score_spread":0.2308391277367356,"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."}}