{"id":"W2971097100","doi":"10.1128/jcm.01281-19","title":"A Cost-Effective Method for Identifying <i>Enterobacterales</i> with OXA-181","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Calgary Laboratory Services","keywords":"Loop-mediated isothermal amplification; Bacteria; Confidence interval; Biology; Enterobacteriaceae; Indian subcontinent; Microbiology; Enzyme; Computational biology; Medicine; Genetics; Escherichia coli; Gene; Internal medicine; Biochemistry","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":[0.0006556039,0.0009643216,0.0005179748,0.001724795,0.000351081,0.0006618599,0.0007993752,0.001015977,0.002792188],"category_scores_gemma":[0.0009493699,0.0003929943,0.0004397975,0.0008909421,0.0002743328,0.0004763058,0.0006242825,0.0004765459,0.001657582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000275355,"about_ca_system_score_gemma":0.000443307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101633,"about_ca_topic_score_gemma":0.002555678,"domain_scores_codex":[0.9987463,0.0002577662,0.000102464,0.0002559683,0.0005635481,0.00007396457],"domain_scores_gemma":[0.9993984,0.0001153596,0.0001574855,0.0000531814,0.0002081685,0.00006743769],"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":[0.0002961247,0.0003683497,0.03623999,0.0005209208,0.00007965779,0.0003335885,0.0001068421,0.0002823085,0.8520425,0.0001504768,0.001318621,0.1082607],"study_design_scores_gemma":[0.0002205444,0.003400133,0.2314679,0.0002214838,0.0007676493,0.01077569,0.0008137207,0.008234262,0.7103416,0.0005219387,0.03305007,0.000185018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.845351,0.00454295,0.1259983,0.001795042,0.0004718641,0.002012013,0.008816575,0.001316106,0.009696197],"genre_scores_gemma":[0.759651,0.002124418,0.2224951,0.0003472604,0.0001134677,0.0007816593,0.006932701,0.000064733,0.007489664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002792188,"threshold_uncertainty_score":0.009340763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535940769555661,"score_gpt":0.4097493129656658,"score_spread":0.3643899052701092,"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."}}