{"id":"W2951789773","doi":"10.5539/jas.v11n9p167","title":"Biomass Accumulation and Industrial Yield of Irrigated Sugarcane Submitted to Sources and Doses of Nitrogen Grown in Cerrado Oxisol","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Sugarcane Cultivation and Processing","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministério da Ciência, Tecnologia, Inovações e Comunicações; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Goiás; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Irrigation; Randomized block design; Oxisol; Sowing; Agronomy; Biomass (ecology); Sugar; Yield (engineering); Environmental science; Ammonium nitrate; Nitrogen; Mathematics; Biology; Chemistry; Soil water","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":[0.0001354178,0.0003703762,0.0003217655,0.0002238121,0.0002085887,0.0003374326,0.0002120715,0.0001500965,0.0004936836],"category_scores_gemma":[0.0001586647,0.00009981917,0.0001602357,0.0003514432,0.0001671928,0.0001619548,0.000193434,0.0002491917,0.0001090227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006900086,"about_ca_system_score_gemma":0.0004215699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01538151,"about_ca_topic_score_gemma":0.02221923,"domain_scores_codex":[0.9999015,0.000009617896,0.000006051007,0.00003600232,0.00001923657,0.00002753988],"domain_scores_gemma":[0.9998457,0.00002590113,0.00003268598,0.000009846424,0.00003833924,0.00004748697],"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":[0.00105162,0.0001702348,0.01262323,0.00008871018,0.00003185272,0.0002164719,0.0002297226,0.0004393506,0.9810431,0.00006584872,0.00005664666,0.003983303],"study_design_scores_gemma":[0.00005521648,0.002920404,0.7177398,0.00002029693,0.0001124013,0.0001442216,0.001061343,0.001942356,0.2726457,0.00007497976,0.003249669,0.0000335501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994125,0.00009214265,0.00003421825,0.000005837495,0.000001951315,0.00000325692,0.0001146892,0.000003743286,0.0003316941],"genre_scores_gemma":[0.9979101,0.0001429218,0.0002201319,0.00001533544,0.000001842549,0.00001316319,0.0005407932,0.000006366261,0.00114945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01538151,"threshold_uncertainty_score":0.03058398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05878355060802189,"score_gpt":0.2619531457255123,"score_spread":0.2031695951174904,"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."}}