{"id":"W4409341990","doi":"10.1051/bioconf/202517201002","title":"Cellulase extraction from <i>Pseudomonas fluorescens</i> for efficient enzymatic hydrolysis and fermentation with <i>Pichia fermentans</i> and <i>Saccharomyces cerevisiae</i> for cellulosic bioethanol production","year":2025,"lang":"en","type":"article","venue":"BIO Web of Conferences","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nuclear Waste Management Organization","funders":"","keywords":"Cellulosic ethanol; Cellulase; Fermentation; Pseudomonas fluorescens; Biofuel; Chemistry; Food science; Hydrolysis; Saccharomyces cerevisiae; Enzymatic hydrolysis; Pseudomonas; Yeast; Pulp and paper industry; Biochemistry; Cellulose; Biotechnology; Biology; Bacteria; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001540149,0.0002165602,0.0002717949,0.0001645072,0.0001253155,0.0000689458,0.00007323233,0.0001018063,0.00001662086],"category_scores_gemma":[0.00001399599,0.0001797702,0.00005045946,0.0001731119,0.0001435811,0.0001347813,0.00001634979,0.00006673978,0.000001012364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002833584,"about_ca_system_score_gemma":0.00007860547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003084288,"about_ca_topic_score_gemma":0.00001487331,"domain_scores_codex":[0.9989666,0.00002629183,0.0003144587,0.0003860834,0.0001363493,0.000170215],"domain_scores_gemma":[0.9995037,0.00006968819,0.0001221475,0.0001532673,0.0000993343,0.00005192004],"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":[0.0003139663,0.0000984639,0.003681496,0.001539773,0.0001490176,2.315418e-7,0.0001850085,0.0001580008,0.9852176,0.0002524914,0.001036176,0.007367787],"study_design_scores_gemma":[0.001306401,0.0001926948,0.001780405,0.0002422325,0.0002907791,0.000002647482,0.0007553979,0.01691523,0.9722518,0.0002778535,0.005744008,0.0002405745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897995,0.001611512,0.005432262,0.0008301265,0.0006058531,0.001322908,0.0000814004,0.0001089439,0.0002074666],"genre_scores_gemma":[0.9971427,0.0007053232,0.001661184,0.00003469906,0.00007031627,0.0001211272,0.0001074237,0.00001443917,0.0001428279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01675723,"threshold_uncertainty_score":0.7330816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066634888975215,"score_gpt":0.2259325037275544,"score_spread":0.2152661548378023,"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."}}