{"id":"W3159467633","doi":"10.1186/s12934-021-01675-3","title":"Automation assisted anaerobic phenotyping for metabolic engineering","year":2021,"lang":"en","type":"article","venue":"Microbial Cell Factories","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Mitacs; National Central University; Virginia Commonwealth University; Genome Canada","keywords":"Laboratory automation; Automation; Throughput; Biochemical engineering; Workflow; Computer science; Bioreactor; Process engineering; Synthetic biology; Metabolic engineering; Biotechnology; Computational biology; Biology; Engineering; Mechanical engineering; Wireless","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":[0.000909786,0.001016352,0.0004958527,0.0009430035,0.0003696825,0.000814201,0.0006796796,0.0005503212,0.001457266],"category_scores_gemma":[0.001791858,0.0003136999,0.0006639074,0.0009736574,0.0004721837,0.0006575414,0.001523682,0.0009683662,0.001115152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005551346,"about_ca_system_score_gemma":0.0006435703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001117214,"about_ca_topic_score_gemma":0.001666488,"domain_scores_codex":[0.9988688,0.0002656815,0.00009159667,0.0002213539,0.0004816152,0.0000709581],"domain_scores_gemma":[0.9986504,0.0004128075,0.0002770248,0.0002671283,0.0003169406,0.00007573905],"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":[0.0002441038,0.0001964019,0.003212188,0.0003666324,0.00003724934,0.0001317522,0.0000835935,0.01883331,0.8885501,0.00155883,0.001425474,0.08536024],"study_design_scores_gemma":[0.00002639564,0.0004038332,0.006859853,0.00006304862,0.00003963649,0.0003452766,0.000071653,0.1388872,0.8333802,0.0033247,0.016508,0.00009025633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1434847,0.0007572195,0.8443284,0.0005359739,0.0001306816,0.0004320531,0.002371174,0.004800675,0.00315911],"genre_scores_gemma":[0.4015893,0.00100322,0.5919027,0.0001452632,0.00004902311,0.0005149179,0.002807316,0.0004153732,0.001572813],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001457266,"threshold_uncertainty_score":0.004875064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007826078957705387,"score_gpt":0.2080895001051976,"score_spread":0.2002634211474922,"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."}}