{"id":"W2910017122","doi":"10.3390/genes10010034","title":"Using a Chemical Genetic Screen to Enhance Our Understanding of the Antimicrobial Properties of Gallium against Escherichia coli","year":2019,"lang":"en","type":"article","venue":"Genes","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Calgary","keywords":"Gallium; Escherichia coli; Antimicrobial; Gene; Biology; Mutant; DNA repair; Genetics; DNA; Microbiology; Biochemistry; Computational biology; Chemistry","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":[0.00007253829,0.0001159455,0.000180639,0.00002859931,0.00003498417,0.000007526311,0.0001891124,0.00009338053,3.527389e-7],"category_scores_gemma":[0.00002174157,0.00008179162,0.0001092592,0.0001493689,0.00006679683,0.000002197893,0.0001628809,0.00004264234,5.836978e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002057011,"about_ca_system_score_gemma":0.00005630465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000217142,"about_ca_topic_score_gemma":0.00001194492,"domain_scores_codex":[0.999268,0.00003470414,0.0002087379,0.0002267516,0.0001079843,0.0001538045],"domain_scores_gemma":[0.999439,0.000001788115,0.0001282353,0.0003195129,0.00008356632,0.00002787233],"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":[0.00005290455,0.00002322727,0.00128721,0.00003638431,0.00003880423,1.425901e-7,0.00002076492,0.0001828664,0.9980413,0.000003208806,0.00005450112,0.0002586357],"study_design_scores_gemma":[0.00007071671,0.00004860058,0.0001266252,0.00009378742,0.00002389269,0.00000171996,0.000122482,0.0001317282,0.9991173,0.000006440184,0.000138966,0.0001177763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962181,0.0002047023,0.003163227,0.0000878571,0.00005534769,0.0001948921,0.0000105423,0.000008147389,0.00005712051],"genre_scores_gemma":[0.9868975,0.00005776116,0.01273741,0.0001402545,0.00006661158,0.000001289569,0.000003901112,0.00001430532,0.00008097174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009574181,"threshold_uncertainty_score":0.3335365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788328222508206,"score_gpt":0.2766246186702047,"score_spread":0.2487413364451226,"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."}}