{"id":"W2327436632","doi":"10.1128/microbe.8.308.1","title":"Database Tracks, Codifies Dynamics of Antibiotic Resistance","year":2013,"lang":"en","type":"article","venue":"Microbe Magazine","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Database; Dynamics (music); Computer science; Resistance (ecology); Antibiotic resistance; Antibiotics; Biology; Physics; Microbiology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005958899,0.000674044,0.001070102,0.01274578,0.001968584,0.01221204,0.001625106,0.00122814,0.02490496],"category_scores_gemma":[0.02949966,0.0008694855,0.0006321926,0.01866911,0.0006659317,0.01069439,0.003277805,0.001986456,0.02795534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002202324,"about_ca_system_score_gemma":0.005516785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02589196,"about_ca_topic_score_gemma":0.03309802,"domain_scores_codex":[0.9953062,0.0006462716,0.001029651,0.0008832242,0.001896286,0.0002383652],"domain_scores_gemma":[0.9682804,0.006814246,0.00373104,0.007820492,0.01021228,0.003141481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002300711,0.00005864353,0.01237292,0.0005371243,0.00005680101,0.00005969955,0.0003476071,0.0002532834,0.001358767,0.01120794,0.8012382,0.1722789],"study_design_scores_gemma":[0.00002681431,0.00002390666,0.008810571,0.0001600012,0.00003083255,0.0001177311,0.0002642672,0.0004967108,0.000875379,0.00336273,0.9857859,0.00004512989],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01632355,0.00839152,0.02650351,0.02857592,0.003702751,0.0007067885,0.7375031,0.02840631,0.1498866],"genre_scores_gemma":[0.03442882,0.009351968,0.0737836,0.005513117,0.001461342,0.0003501706,0.811799,0.004232378,0.05907962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02589196,"threshold_uncertainty_score":0.08331543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006833809854811703,"score_gpt":0.2242109692087539,"score_spread":0.2173771593539422,"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."}}