{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004126872,0.0007788181,0.0006281799,0.0005827239,0.0003119421,0.0007860958,0.0005205604,0.000449953,0.002701286],"category_scores_gemma":[0.001092202,0.0004243705,0.0003965858,0.0006674904,0.0002836596,0.0006135219,0.0009338498,0.0008354982,0.001482752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002993768,"about_ca_system_score_gemma":0.0003890789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164254,"about_ca_topic_score_gemma":0.001272543,"domain_scores_codex":[0.9992852,0.0001620015,0.00004010249,0.0001304786,0.00031876,0.00006346501],"domain_scores_gemma":[0.9992672,0.0003100458,0.00003917629,0.0002651616,0.00009868606,0.00001974881],"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":[0.0006877839,0.0001604022,0.002513194,0.0002386362,0.00004750377,0.0001313478,0.0001117638,0.03172434,0.6724423,0.005847167,0.005721978,0.2803736],"study_design_scores_gemma":[0.00004980569,0.0001331846,0.007345629,0.00002175818,0.00003627154,0.0002451851,0.00003688598,0.3863746,0.5637458,0.01381693,0.02813282,0.00006112181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08321831,0.0004511761,0.8969787,0.0002710767,0.0001627009,0.00008159562,0.001661147,0.01160015,0.005575071],"genre_scores_gemma":[0.5186414,0.0004094379,0.4719961,0.00009352028,0.00008345988,0.0001720162,0.003215772,0.0007585731,0.004629749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002701286,"threshold_uncertainty_score":0.00903672,"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."}}