{"id":"W4401107522","doi":"10.1139/cjm-2024-0049","title":"Identification of key drivers of antimicrobial resistance in <i>Enterococcus</i> using machine learning","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Microbiology","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Dalhousie University","funders":"Beef Cattle Research Council; Agriculture and Agri-Food Canada; Government of Canada","keywords":"Enterococcus; Antimicrobial; Microbiology; Identification (biology); Key (lock); Antibiotic resistance; Biology; Antibiotics; Botany; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008848128,0.0006518249,0.0005954945,0.0009319116,0.0003390882,0.001861376,0.0003607864,0.0004889751,0.0008151528],"category_scores_gemma":[0.002289807,0.0002207278,0.0005364206,0.0009473924,0.0002638761,0.0007276665,0.0006448965,0.0006417133,0.0004509204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009908401,"about_ca_system_score_gemma":0.001144823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007804105,"about_ca_topic_score_gemma":0.01290806,"domain_scores_codex":[0.9995458,0.000102797,0.00003484299,0.0001364508,0.0001116217,0.00006849895],"domain_scores_gemma":[0.999332,0.0002419658,0.0001713049,0.00005694423,0.0001501197,0.00004767707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004080456,0.0003428327,0.4196313,0.0008481971,0.0003100176,0.000503821,0.0003993812,0.07374385,0.2444047,0.003020909,0.00328962,0.2530974],"study_design_scores_gemma":[0.00001976574,0.0004806564,0.3380865,0.0003081763,0.0002791713,0.000716346,0.001189231,0.5266277,0.1078804,0.009411935,0.01484276,0.0001574096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371297,0.00269855,0.05041227,0.001185646,0.00004067118,0.00008403564,0.003261222,0.0006220103,0.00456584],"genre_scores_gemma":[0.9672153,0.0009142173,0.0283503,0.0001618548,0.00001353684,0.00003237652,0.002611893,0.00005082663,0.0006497112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007804105,"threshold_uncertainty_score":0.01551735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059567421410857,"score_gpt":0.2331355762890942,"score_spread":0.2225399020749856,"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."}}