{"id":"W2763159004","doi":"10.1016/j.biortech.2017.09.132","title":"Hydrolytic pre-treatment methods for enhanced biobutanol production from agro-industrial wastes","year":2017,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre de Recherche Industrielle du Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Chemistry; Furfural; Hydrolysate; Hydrolysis; Pomace; Fermentation; Butanol; Levulinic acid; Starch; Yield (engineering); Biofuel; Food science; Pulp and paper industry; Organic chemistry; Waste management; Ethanol; Catalysis; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001319871,0.000232601,0.0002653344,0.0002073863,0.0003048041,0.00005507681,0.0003630858,0.0005382706,0.00003589221],"category_scores_gemma":[0.0002159127,0.0001996513,0.0000908478,0.0001056061,0.000215474,0.00008880263,0.00007457531,0.0001579093,0.00004117855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001445737,"about_ca_system_score_gemma":0.00001544075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004965161,"about_ca_topic_score_gemma":0.000005936857,"domain_scores_codex":[0.998925,0.00002221502,0.0002184203,0.0004596781,0.00006518549,0.0003094815],"domain_scores_gemma":[0.9988554,0.00002797182,0.0001127948,0.0009130668,0.00003759013,0.00005315664],"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":[0.00005512979,0.00003768006,0.0002600163,0.00001622608,0.00007503842,5.326053e-7,0.0000661645,0.00003261926,0.7030711,0.00002479796,0.0005027181,0.295858],"study_design_scores_gemma":[0.0007843179,0.0002066477,0.0001465798,0.00002103388,0.0000551342,0.000004412582,0.00009553251,0.00232705,0.8738769,0.000743398,0.1215333,0.0002055886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976909,0.000687012,0.01374025,0.003275404,0.002435409,0.00109475,0.00003176,0.001590444,0.0002359152],"genre_scores_gemma":[0.9830928,0.0001051274,0.01524838,0.000009918465,0.0007818973,0.0001587959,0.00002331272,0.00003877161,0.0005410024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2956524,"threshold_uncertainty_score":0.8141543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02876169839636017,"score_gpt":0.295302014973445,"score_spread":0.2665403165770848,"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."}}