{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001302718,0.0005620744,0.0003523591,0.000286151,0.0001668907,0.0003384179,0.0001887919,0.0002971143,0.0005498391],"category_scores_gemma":[0.0001522535,0.0001786124,0.0003492241,0.0003712164,0.0001250064,0.0002267531,0.0002019584,0.0004765942,0.0003119581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001956594,"about_ca_system_score_gemma":0.000196909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001597109,"about_ca_topic_score_gemma":0.001800025,"domain_scores_codex":[0.9998153,0.00001371598,0.00001570857,0.0000336726,0.00007578822,0.00004584674],"domain_scores_gemma":[0.9999355,0.00001187537,0.00001596548,0.000007382168,0.00001880124,0.00001044085],"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":[0.000010536,0.00001418339,0.0001165504,0.00002367572,0.000001845252,0.000031794,0.000004499584,0.00002636361,0.9989817,0.00001205912,0.00001292501,0.0007640317],"study_design_scores_gemma":[0.000003078013,0.00008218887,0.003289197,0.000007033839,0.000009789043,0.0001401564,0.00001889416,0.0002685577,0.9950624,0.00001424145,0.001099711,0.000004694542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805115,0.001637172,0.01364984,0.0001520493,0.00003185683,0.0001306049,0.0009905373,0.0001347027,0.002761754],"genre_scores_gemma":[0.9741657,0.002077789,0.01564698,0.0001088566,0.00001842248,0.0001194255,0.003535843,0.00005426072,0.004272646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001597109,"threshold_uncertainty_score":0.003175616,"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."}}