{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374709,0.0006000332,0.0005020218,0.0007614963,0.0002125415,0.0004925982,0.0004443873,0.000530039,0.0009772186],"category_scores_gemma":[0.0004417761,0.0002412441,0.0005924236,0.0005962566,0.0003612367,0.0002907906,0.0004093764,0.0007003069,0.0005177391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734603,"about_ca_system_score_gemma":0.000521145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344857,"about_ca_topic_score_gemma":0.002042445,"domain_scores_codex":[0.9995053,0.0001123679,0.00006550827,0.00006606879,0.0001800747,0.00007065329],"domain_scores_gemma":[0.9996305,0.0001663592,0.00006786555,0.00005150496,0.00005241218,0.0000312781],"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":[0.00003081576,0.00005629685,0.0001969548,0.00003745414,0.000006151301,0.00009928985,0.00001161838,0.00007974313,0.9982945,0.00009118042,0.00002145594,0.001074471],"study_design_scores_gemma":[0.00001012755,0.0004368802,0.003559124,0.00001149641,0.00004298141,0.0006010869,0.00005663512,0.0008459632,0.991847,0.00007427626,0.002504352,0.000009916013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376099,0.00155122,0.05085634,0.000738308,0.00007918819,0.000443678,0.003074088,0.0004571099,0.005190283],"genre_scores_gemma":[0.9299393,0.00249786,0.05912371,0.0003532882,0.00002087558,0.0001864237,0.003060143,0.0001230899,0.004695286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001344857,"threshold_uncertainty_score":0.003269136,"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."}}