{"id":"W2740735410","doi":"10.1016/j.biortech.2017.07.107","title":"A two-step optimization strategy for 2nd generation ethanol production using softwood hemicellulosic hydrolysate as fermentation substrate","year":2017,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ethanol fuel; Softwood; Hydrolysate; Fermentation; Biomass (ecology); Raw material; Chemistry; Pulp and paper industry; Ethanol fermentation; Ethanol; Cellulosic ethanol; Yeast; Hydrolysis; Factorial experiment; Enzymatic hydrolysis; Food science; Biochemistry; Organic chemistry; Cellulose; Mathematics; Agronomy; Engineering; Biology","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":[0.0001901517,0.0008767633,0.0007608837,0.0003312815,0.0002655051,0.0005991859,0.0004739953,0.0004272561,0.0005639104],"category_scores_gemma":[0.0001188097,0.0002787736,0.0006136301,0.0004827107,0.0001300953,0.0004439244,0.0004615625,0.0005975096,0.0002666611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003985921,"about_ca_system_score_gemma":0.0004919462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001528834,"about_ca_topic_score_gemma":0.003127554,"domain_scores_codex":[0.9998497,0.00001453734,0.00001557645,0.00003739687,0.00004637244,0.00003644246],"domain_scores_gemma":[0.9999622,0.000005432322,0.000008767537,0.00000547118,0.000009983378,0.00000810148],"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":[0.0001687347,0.0002708591,0.0003611202,0.0001505427,0.00002776686,0.0001456146,0.00002057704,0.004922645,0.9816664,0.0001943256,0.00008336451,0.01198808],"study_design_scores_gemma":[0.00004137884,0.0006891149,0.001780327,0.000009576349,0.00006883645,0.0001193949,0.00004340512,0.02344091,0.9717166,0.00009819488,0.001956972,0.00003531804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704498,0.001488218,0.02504614,0.0001269246,0.0000656177,0.0001437947,0.0002436266,0.0001031075,0.002332753],"genre_scores_gemma":[0.9687268,0.001065591,0.0274403,0.00003761751,0.000009132856,0.00009412566,0.0003079605,0.00002359138,0.002294936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001528834,"threshold_uncertainty_score":0.003039896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0373171972185497,"score_gpt":0.2735687762720135,"score_spread":0.2362515790534638,"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."}}