{"id":"W4212982368","doi":"10.1101/2022.02.13.480257","title":"Faster growth enhances low carbon fuel and chemical production through gas fermentation","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eesti Teadusagentuur; Australian Research Council; European Commission; Australian Government; Suncor Energy Incorporated","keywords":"Bioprocess; Fermentation; Metabolic engineering; Bioprocess engineering; Chemostat; Biochemical engineering; Bioreactor; Biomass (ecology); Metabolic flux analysis; Biology; Chemistry; Metabolism; Biochemistry; Biotechnology; Bacteria; Engineering; Gene; Ecology; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000272066,0.0003863092,0.0002894844,0.00007947597,0.00008745484,0.0000743819,0.0002029573,0.0003268884,0.00001346976],"category_scores_gemma":[0.0001292667,0.0004237229,0.00007981501,0.0001743852,0.00009373424,0.00001296515,0.0003780579,0.0003914325,0.000002388188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007397621,"about_ca_system_score_gemma":0.000115777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004142141,"about_ca_topic_score_gemma":9.600138e-7,"domain_scores_codex":[0.9980011,0.00008122368,0.0003149539,0.001085399,0.0002066403,0.0003106947],"domain_scores_gemma":[0.9989231,0.000002113087,0.000206657,0.0006018041,0.0001845839,0.00008172233],"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":[0.00004633102,0.00007362558,0.0004501089,0.0004287734,0.00008259912,0.000002007381,0.00001851192,0.0000482809,0.99868,0.00002828845,0.0001381417,0.000003319395],"study_design_scores_gemma":[0.0002215676,0.00005855757,0.003216389,0.00006790472,0.00007067684,1.331357e-7,0.000006330265,0.00002455494,0.993008,0.000002999374,0.002851686,0.0004712527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947004,0.002159657,0.0001988813,0.0002474671,0.002067985,0.0004646039,0.00005222001,0.00009594207,0.00001288716],"genre_scores_gemma":[0.9939778,0.002003814,0.001927561,0.00007662152,0.001726375,0.0001747518,0.00000624115,0.00007627194,0.00003056187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005672064,"threshold_uncertainty_score":0.9998215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006634989512328836,"score_gpt":0.2053229985295063,"score_spread":0.1986880090171775,"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."}}