{"id":"W4246541623","doi":"10.21203/rs.3.rs-526133/v1","title":"Automation Assisted Anaerobic Phenotyping For Metabolic Engineering","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Mitacs; National Central University; Virginia Commonwealth University; Genome Canada","keywords":"Metabolic engineering; Automation; Anaerobic exercise; Biochemical engineering; Engineering; Biotechnology; Computer science; Systems engineering; Biology; Mechanical engineering; Biochemistry; Physiology; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00104393,0.0002491981,0.0003052359,0.0002480119,0.0001297654,0.0001655802,0.0002654306,0.0004596792,0.00001420635],"category_scores_gemma":[0.0008955167,0.0002656881,0.0002294262,0.0002825131,0.00003280386,0.000005089988,0.0004487266,0.0005216508,0.000004258296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005119385,"about_ca_system_score_gemma":0.0002470613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005322261,"about_ca_topic_score_gemma":0.00001884662,"domain_scores_codex":[0.9980705,0.0001293501,0.000280531,0.0007323331,0.0002878359,0.0004993933],"domain_scores_gemma":[0.998437,0.00001578766,0.00007061833,0.0006916712,0.0006802421,0.0001046694],"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":[0.00002338205,0.00005119397,0.00001971734,0.0009256481,0.000137635,0.000001292291,0.00005530091,0.008858418,0.9805554,0.0001418566,0.0008874843,0.008342643],"study_design_scores_gemma":[0.0006079531,0.0001325889,0.007372914,0.0007027122,0.00007607832,0.00002118287,0.000145237,0.01110051,0.8503851,0.00003253254,0.1287106,0.0007124977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9051419,0.01700526,0.07421858,0.0002756926,0.001585025,0.001439546,0.0001199942,0.0001451785,0.00006886419],"genre_scores_gemma":[0.9753609,0.001192334,0.01759385,0.00001536195,0.002334465,0.0004074242,0.002472376,0.00008706996,0.0005361852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1301703,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03561921934896303,"score_gpt":0.3391562783552929,"score_spread":0.3035370590063299,"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."}}