{"id":"W2342573408","doi":"10.1007/s11274-016-2056-x","title":"Consolidating biofuel platforms through the fermentative bioconversion of crude glycerol to butanol","year":2016,"lang":"en","type":"review","venue":"World Journal of Microbiology and Biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; BioFuelNet Canada; Alexander von Humboldt-Stiftung","keywords":"Bioconversion; Glycerol; Biodiesel; Biofuel; Pulp and paper industry; Raw material; Biodiesel production; Bioenergy; Diesel fuel; Commodity chemicals; Butanol; Chemistry; Waste management; Biotechnology; Food science; Organic chemistry; Fermentation; Catalysis; Biology; Ethanol; Engineering","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":[0.0003587137,0.000340549,0.001020191,0.0003441504,0.00009798901,0.000007599238,0.0004749348,0.000742158,0.00001824818],"category_scores_gemma":[0.0001271228,0.0001789351,0.0002776328,0.0003090845,0.0005863581,0.000006391518,0.0002801585,0.0004545565,0.00000934117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002787691,"about_ca_system_score_gemma":0.0001127115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003621326,"about_ca_topic_score_gemma":0.000007715968,"domain_scores_codex":[0.9984269,0.0000909609,0.0008010116,0.0003485545,0.00004139385,0.0002912202],"domain_scores_gemma":[0.9985613,0.00003642823,0.0009202418,0.0003236175,0.0001143772,0.00004400006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007664567,0.00002531998,0.000002255038,0.0004285192,0.0003454665,0.000003264465,0.00003009961,2.815706e-7,0.7868873,0.0001735188,0.001003494,0.2110238],"study_design_scores_gemma":[0.0002698502,0.0003942477,5.803633e-7,0.00122319,0.0001564637,0.001113653,0.00004433616,1.4967e-8,0.2993537,0.00004223307,0.697244,0.0001576994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01493022,0.9821699,0.0007768581,0.0007826455,0.000749602,0.0004206843,0.0001405895,0.00001030407,0.00001921429],"genre_scores_gemma":[0.006326357,0.9916921,0.0011681,0.000131979,0.0003361816,0.000006724416,0.00003228473,0.00002546463,0.0002808331],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6962405,"threshold_uncertainty_score":0.7296762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400516378810715,"score_gpt":0.2800267431993758,"score_spread":0.2660215794112686,"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."}}