{"id":"W2936915583","doi":"10.5539/jas.v11n5p93","title":"Effect of Integrated Nutrient Management on Yield and Quality of Basmati Rice Varieties","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microbial inoculant; Yield (engineering); Mathematics; Nutrient management; Agronomy; Amylose; Nutrient; Manure; Non-invasive ventilation; Biotechnology; Horticulture; Biology; Forensic science; Veterinary medicine; Food science; Medicine; Starch; Materials science","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":[0.001274249,0.0001039218,0.0002604753,0.00003036515,0.00006883621,0.00003685332,0.0002963017,0.00003320605,0.00008400169],"category_scores_gemma":[0.0001613422,0.00002790478,0.00008515933,0.0006553541,0.0001255905,0.0002537553,0.00006804633,0.0001035473,0.000002653437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003834315,"about_ca_system_score_gemma":0.000008044275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007749032,"about_ca_topic_score_gemma":0.000005219471,"domain_scores_codex":[0.9985923,0.00006487684,0.0004581178,0.0001407853,0.0006023875,0.0001415777],"domain_scores_gemma":[0.9986824,0.0003222135,0.0005718322,0.00004289576,0.0003034502,0.00007723884],"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":[0.00009171241,0.00007757272,0.006120369,0.0000588804,0.00001274065,2.699528e-7,0.0001067125,0.00001166736,0.9846585,0.001582787,0.0001091341,0.007169595],"study_design_scores_gemma":[0.0002174586,0.00198874,0.6912249,0.0001044675,0.00001004035,0.000003175099,0.001100797,0.000003725936,0.3050663,0.00003245062,0.0001811465,0.00006678121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973895,0.00004225334,0.000004459019,0.000421295,0.0001720036,0.0002137848,0.000004309887,0.000004342578,0.001748071],"genre_scores_gemma":[0.9995311,0.00005493851,0.00006715751,0.00005981179,0.00002430257,0.000001216158,0.000001228208,2.4229e-7,0.0002600134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6851045,"threshold_uncertainty_score":0.1137924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001157454486615,"score_gpt":0.2494459406869,"score_spread":0.2294343661420339,"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."}}