{"id":"W2987614117","doi":"10.5539/jas.v11n18p117","title":"Can the Nitrogen and Silicon Increase the Productivity and Yield in Rice Crops in the Rainfed Environment?","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Silicon Effects in Agriculture","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Sowing; Agronomy; Nitrogen; Yield (engineering); Randomized block design; Chlorophyll; Human fertilization; Postharvest; Silicon; Chemistry; Biology; Horticulture; Materials science; Metallurgy","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":[0.002703698,0.0001562203,0.0001858103,0.00001806962,0.0003332996,0.0002358802,0.0008256632,0.00005478668,0.00001896056],"category_scores_gemma":[0.0004312067,0.0000281364,0.00005269102,0.000904653,0.0004915664,0.0004339591,0.0001771013,0.0004541471,0.000002913021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006308004,"about_ca_system_score_gemma":0.00001665734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008438465,"about_ca_topic_score_gemma":0.001913194,"domain_scores_codex":[0.9982789,0.0003102808,0.000269714,0.0002663412,0.0005618216,0.0003129908],"domain_scores_gemma":[0.9985556,0.000971409,0.0002427919,0.00009024476,0.00005872743,0.00008122487],"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":[0.00002180668,0.00006817615,0.1930491,0.000004392321,0.000004924535,0.000006055715,0.001451987,0.00003941175,0.8007301,0.0001227799,0.0002052069,0.004296042],"study_design_scores_gemma":[0.0001360016,0.0001889814,0.9914123,0.0000259043,0.000009212181,0.0003505063,0.004104704,0.00000677354,0.002995957,0.0002367439,0.0004325788,0.0001003291],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777883,0.0005868183,2.204757e-8,0.02084019,0.00008307748,0.0004894328,0.000002968104,0.000003701518,0.000205435],"genre_scores_gemma":[0.9992403,0.0001102273,0.000006245378,0.0004105989,0.0001744795,0.000007773984,5.598715e-7,4.329556e-7,0.00004941972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7983631,"threshold_uncertainty_score":0.2563505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008124871280724501,"score_gpt":0.1930822728947525,"score_spread":0.184957401614028,"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."}}