{"id":"W2981383407","doi":"10.1371/annotation/76d35829-07a2-479f-bbc1-cce6755b6d8c","title":"Correction: Identification of Small Molecule Inhibitors of Pseudomonas aeruginosa Exoenzyme S Using a Yeast Phenotypic Screen","year":2008,"lang":"en","type":"article","venue":"PLoS Genetics","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Toronto","funders":"National Cancer Institute; Genome Canada; Ontario Genomics; Canadian Institutes of Health Research; Genentech; Ontario Genomics Institute","keywords":"Exoenzyme; Pseudomonas aeruginosa; Biology; Phenotype; Identification (biology); Small molecule; Phenotypic screening; Computational biology; Yeast; Microbiology; Genetics; Bacteria; Gene; Botany","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":[0.00007181318,0.0001084882,0.0002418566,0.0001038366,0.00004656682,0.000003665423,0.00007606662,0.0000844475,0.00004677008],"category_scores_gemma":[0.00007910344,0.00009715329,0.00007752456,0.0002572845,0.0001195325,0.00002067239,0.0000364341,0.00009640249,0.000006733734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000289752,"about_ca_system_score_gemma":0.00008711993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004621359,"about_ca_topic_score_gemma":0.00000968923,"domain_scores_codex":[0.9990467,0.00002963209,0.0004006666,0.0001992307,0.0001964057,0.0001273225],"domain_scores_gemma":[0.9991486,0.00001475161,0.0002252648,0.0002758766,0.0002802527,0.00005523058],"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":[0.00005819465,0.0001740009,0.002472208,0.00008508206,0.00005236421,0.000004210871,0.0001731477,0.00003512202,0.9953408,0.000004369136,0.0006536872,0.0009467897],"study_design_scores_gemma":[0.0002774478,0.0001011741,0.003742992,0.0001029999,0.0001577006,0.00006031305,0.00005764209,0.002140852,0.9931549,0.000003871552,0.0001102994,0.00008980764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981592,0.0009198477,0.0001144651,0.0000956688,0.000265325,0.0002728902,0.00001856921,0.00001835566,0.0001356859],"genre_scores_gemma":[0.9976934,0.0001204842,0.001591745,0.00003153228,0.0002409798,4.425271e-7,0.00001392922,0.00001783481,0.0002896586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002185924,"threshold_uncertainty_score":0.3961796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03682368846879083,"score_gpt":0.2393194403030668,"score_spread":0.202495751834276,"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."}}