{"id":"W3212079120","doi":"10.3389/fmicb.2021.776967","title":"Systematic Evaluation of Whole-Genome Sequencing Based Prediction of Antimicrobial Resistance in Campylobacter jejuni and C. coli","year":2021,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Public Health Agency of Canada; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency","funders":"Genome Alberta; Public Health Agency; Public Health Agency of Canada; Canadian Food Inspection Agency; Canadian Poultry Research Council","keywords":"Florfenicol; Resistome; Biology; Antibiotic resistance; Genome; Tetracycline; Campylobacter jejuni; Campylobacter; Microbiology; Campylobacter coli; Genetics; Gene; Antibiotics; Mobile genetic elements; Bacteria","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002779229,0.0008186143,0.0009012625,0.0009878136,0.0005032945,0.0009234609,0.0007198934,0.0006404521,0.0003922967],"category_scores_gemma":[0.004321205,0.0003878252,0.0008956156,0.0009820758,0.0003726865,0.0004182734,0.0006319748,0.0004042934,0.0001935419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008667645,"about_ca_system_score_gemma":0.001661414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01497057,"about_ca_topic_score_gemma":0.0248064,"domain_scores_codex":[0.999001,0.0002920278,0.00006028294,0.0003202252,0.0002611442,0.00006539148],"domain_scores_gemma":[0.9977942,0.001213535,0.0002269422,0.0001665603,0.0004763447,0.0001224284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0041559,0.0007086427,0.1954663,0.001556606,0.001883543,0.0005250546,0.0007422285,0.1916931,0.4961755,0.0003648758,0.001711986,0.1050163],"study_design_scores_gemma":[0.0001683778,0.001925476,0.2779069,0.0001004639,0.001068066,0.0003961586,0.0006030992,0.5853112,0.1279069,0.0004186479,0.004054793,0.0001400212],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855472,0.000880224,0.01007754,0.00007993092,0.00001526765,0.0001095755,0.002356191,0.0004822185,0.0004517982],"genre_scores_gemma":[0.9250985,0.0005911001,0.05540328,0.0001416179,0.000009596194,0.0001367295,0.01747677,0.0002453882,0.0008970692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01497057,"threshold_uncertainty_score":0.02976686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02414123363335131,"score_gpt":0.2237575753750976,"score_spread":0.1996163417417463,"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."}}