{"id":"W3027917787","doi":"10.1007/s13399-020-00774-2","title":"Effect of fuel composition uncertainty on grate firing biomass combustor performance: a Bayesian model averaging approach","year":2020,"lang":"en","type":"article","venue":"Biomass Conversion and Biorefinery","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Combustion; Combustor; Biomass (ecology); Solid fuel; Environmental science; Uncertainty analysis; Heat of combustion; Waste management; Biofuel; Fuel efficiency; Process engineering; Engineering; Chemistry; Simulation; Automotive engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001665176,0.0003597526,0.000415242,0.0001794716,0.00009329848,0.00003423787,0.0002149754,0.0001936198,0.00002840543],"category_scores_gemma":[0.0000146244,0.0002985966,0.0001149949,0.0004116226,0.0001365687,0.0001662308,0.00010163,0.000171103,0.00001735528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006642658,"about_ca_system_score_gemma":0.00001857953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005149201,"about_ca_topic_score_gemma":1.084611e-7,"domain_scores_codex":[0.9986303,0.0000520646,0.0003246894,0.000398267,0.0002851372,0.0003094978],"domain_scores_gemma":[0.9993279,0.00009353746,0.00009342081,0.0001927636,0.00004637746,0.000246023],"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":[0.0004904255,0.00003542366,0.001083085,0.003629185,0.00006736851,0.000005482043,0.0001829122,0.0008585081,0.9892733,0.00002106175,0.0006166273,0.003736629],"study_design_scores_gemma":[0.001230994,0.0002832325,0.00008411804,0.0000974242,0.00004946587,0.000004173189,0.00002874411,0.3198247,0.6779808,0.00000407241,0.0001341472,0.0002781232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969515,0.0003929398,0.00119529,0.0002076345,0.0001355928,0.000242696,0.00004565536,0.0003619588,0.0004667556],"genre_scores_gemma":[0.9991245,0.0001215839,0.0003899154,0.0001520372,0.00004216723,0.00001195192,0.0001074173,0.00003863568,0.00001176556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3189662,"threshold_uncertainty_score":0.9999466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039072369282026,"score_gpt":0.1964455826080221,"score_spread":0.1860548589152018,"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."}}