{"id":"W2969068731","doi":"10.1002/ese3.394","title":"Enhancing biomass hydrolysis for biofuel production through hydrodynamic modeling and reactor design","year":2019,"lang":"en","type":"article","venue":"Energy Science & Engineering","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Impeller; Computational fluid dynamics; Slurry; Mixing (physics); Enzymatic hydrolysis; Materials science; Chemistry; Mechanics; Hydrolysis; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.000414894,0.0002029927,0.0001692168,0.0002667579,0.000113868,0.00007616068,0.0001757629,0.00008404223,0.000006788368],"category_scores_gemma":[0.00005138511,0.0001995892,0.00004702312,0.0005886216,0.00005110447,0.000857383,0.00004108685,0.0000950346,0.000007564902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351737,"about_ca_system_score_gemma":0.0000275257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002861087,"about_ca_topic_score_gemma":0.000002808478,"domain_scores_codex":[0.9986622,0.000006435463,0.0002081471,0.0004954879,0.0002141526,0.0004135409],"domain_scores_gemma":[0.9995403,0.00002196481,0.00002776224,0.000270176,0.00005483945,0.00008493051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003251932,0.000003653764,0.0000110476,0.00002854906,0.000007131405,1.559369e-7,0.00008929241,0.3538536,0.6449212,0.0008123983,0.000003352362,0.0002662738],"study_design_scores_gemma":[0.00009610206,0.00002161829,0.00001315918,0.00002256876,0.000007395447,0.00000720755,0.00004015787,0.5985479,0.3983945,0.00007353575,0.002593532,0.0001822896],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6307759,0.0003656581,0.3669246,0.00005980989,0.001325858,0.0001533337,0.000001146225,0.000338634,0.00005504238],"genre_scores_gemma":[0.9822529,0.0001039903,0.01729104,0.00001344585,0.0001783019,0.00002521212,0.000004169502,0.00003566554,0.00009525593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.351477,"threshold_uncertainty_score":0.8139013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020310106182687,"score_gpt":0.1953420631754829,"score_spread":0.185138962113656,"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."}}