{"id":"W2804424607","doi":"10.1089/ees.2017.0520","title":"An Environmental Science and Engineering Framework for Combating Antimicrobial Resistance","year":2018,"lang":"en","type":"article","venue":"Environmental Engineering Science","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Alberta","funders":"National Science Foundation","keywords":"Resistance (ecology); Identification (biology); Agriculture; Antibiotic resistance; Environmental planning; Public health; Engineering ethics; Engineering; Risk analysis (engineering); Environmental resource management; Medicine; Ecology; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001083579,0.0003824658,0.0002283862,0.000142787,0.0009705785,0.0002209451,0.000925246,0.00008751465,0.0002978602],"category_scores_gemma":[0.0002236361,0.0003903584,0.00004728606,0.0005406459,0.00508484,0.001638852,0.0006294199,0.0002578218,0.0001524334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008082232,"about_ca_system_score_gemma":0.00002473463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005521347,"about_ca_topic_score_gemma":9.525083e-7,"domain_scores_codex":[0.9963328,0.00001184286,0.0003190347,0.001127051,0.001028835,0.001180395],"domain_scores_gemma":[0.9985485,0.00009459599,0.00008999523,0.0005089814,0.000003730965,0.0007541855],"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":[0.00001712494,0.0001291328,0.005232009,0.00001375249,0.000004036239,0.000003468971,0.0002919234,0.001151047,0.9910198,0.0005054149,0.0000288999,0.001603337],"study_design_scores_gemma":[0.0006480323,0.0003687518,0.241223,0.00009988208,0.0000293978,0.00005411156,0.0001279218,0.08230367,0.6700405,0.000189416,0.003895436,0.0010199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988793,0.00003979815,0.00979074,0.0001194302,0.0003741691,0.0004220183,0.00004818594,0.0001141506,0.0002985776],"genre_scores_gemma":[0.9334312,0.00002461093,0.06590831,0.0003505934,0.0001598742,0.00001322804,0.000005160918,0.00004652176,0.00006050949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3209794,"threshold_uncertainty_score":0.9998548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00867833925122346,"score_gpt":0.2430691466010543,"score_spread":0.2343908073498308,"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."}}