{"id":"W4211261214","doi":"10.3390/antibiotics11020226","title":"Canadian Collaboration to Identify a Minimum Dataset for Antimicrobial Use Surveillance for Policy and Intervention Development across Food Animal Sectors","year":2022,"lang":"en","type":"article","venue":"Antibiotics","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Fisheries and Oceans Canada; Canadian Food Inspection Agency; Agriculture and Agri-Food Canada; Ministry of Agriculture, Food and Rural Affairs; Public Health Agency of Canada","funders":"","keywords":"Intervention (counseling); Antimicrobial; Policy development; Business; Environmental health; Medicine; Biology; Nursing; Microbiology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.05504275,0.001214844,0.001776059,0.01172556,0.007066438,0.005746685,0.005231663,0.001818241,0.01163394],"category_scores_gemma":[0.1006368,0.001156491,0.002783243,0.01499758,0.001164403,0.002186822,0.007931211,0.003577983,0.00248829],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09665366,"about_ca_system_score_gemma":0.4775539,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9829359,"about_ca_topic_score_gemma":0.9854689,"domain_scores_codex":[0.9571657,0.007943479,0.004445786,0.002861415,0.02201277,0.005570843],"domain_scores_gemma":[0.7871678,0.01225923,0.008152066,0.01468524,0.1613699,0.01636571],"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":[0.0006979423,0.0001801619,0.07170188,0.004012564,0.0007603097,0.000188901,0.002380315,0.003036357,0.001595518,0.02587767,0.7677744,0.121794],"study_design_scores_gemma":[0.0001533879,0.00009633719,0.1325158,0.003548915,0.0002442486,0.00006655407,0.001512264,0.00256831,0.001573776,0.003218029,0.8543091,0.0001933074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.02138976,0.004986322,0.03596754,0.04211384,0.002241179,0.008628658,0.8019737,0.002229676,0.08046934],"genre_scores_gemma":[0.1220852,0.004192394,0.1866502,0.01164311,0.0003708548,0.009818945,0.6376813,0.0009752169,0.02658275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9033464,"threshold_uncertainty_score":0.7012746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590414141612529,"score_gpt":0.3492794991229053,"score_spread":0.31337535770678,"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."}}