{"id":"W2023488624","doi":"10.5539/jas.v2n1p144","title":"Statistical Analysis of Main and Interaction Effects to Optimize Xylanase Production under Submerged Cultivation Conditions","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Indian Institute of Technology Madras","keywords":"Xylanase; Interaction; Response surface methodology; Substrate (aquarium); Production (economics); Analysis of variance; Variance (accounting); Main effect; Statistical analysis; Environmental science; Statistics; Mathematics; Biological system; Agricultural engineering; Food science; Biotechnology; Biochemical engineering; Chemistry; Biology; Ecology; Engineering; Enzyme; Biochemistry","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":[0.0002948942,0.00005930685,0.0001177192,0.0002709102,0.00007548404,0.00003229478,0.00006554786,0.0000288713,0.00002931194],"category_scores_gemma":[0.0001787053,0.00003628232,0.00003480384,0.001044034,0.00009211126,0.0004444643,0.00001190085,0.0001534085,0.000001591314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005475463,"about_ca_system_score_gemma":0.00001198761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001028614,"about_ca_topic_score_gemma":0.00001701941,"domain_scores_codex":[0.999382,0.00001536727,0.000195564,0.00009864386,0.0002188268,0.00008955852],"domain_scores_gemma":[0.9995078,0.00002454759,0.0000901063,0.00005032392,0.0002210456,0.0001061255],"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":[0.00000903925,0.00001881348,0.0006902563,0.00001565214,0.00003946365,3.943748e-7,0.0001212513,0.01432267,0.9835226,0.00009263367,0.0007800921,0.0003871208],"study_design_scores_gemma":[0.00009595656,0.00005772618,0.8223801,0.00001166129,0.0001267342,0.00003360699,0.0003564683,0.00123075,0.175562,0.00002050789,0.00006248086,0.00006195084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961661,0.000007120706,0.002088166,0.0006524954,0.0009588018,0.00008212981,0.000004220245,0.00001120593,0.00002977203],"genre_scores_gemma":[0.9968452,0.00001459568,0.003006602,0.0000144277,0.00009018261,9.832974e-7,0.000004550399,0.000001481113,0.00002197605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8216898,"threshold_uncertainty_score":0.147955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0067545461911457,"score_gpt":0.2448342964012933,"score_spread":0.2380797502101475,"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."}}