{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003628125,0.0001586964,0.0001487083,0.00005826986,0.0006071677,0.0001513747,0.0001771184,0.00004463507,0.00005966076],"category_scores_gemma":[0.0001350389,0.0001733447,0.00003992577,0.0002755503,0.00008349602,0.0002492309,0.0003541593,0.0000719296,0.00002025106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007133916,"about_ca_system_score_gemma":0.00006281752,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006336777,"about_ca_topic_score_gemma":0.0716389,"domain_scores_codex":[0.9986343,0.00004328047,0.0002523918,0.0003775188,0.0001671357,0.0005253701],"domain_scores_gemma":[0.9993874,0.00007120447,0.00007204615,0.0001416818,0.000009359494,0.0003182894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007305421,0.000898462,0.3469604,0.0002668194,0.0001655264,0.00001796333,0.002777028,0.001555673,0.5327845,0.0002885589,0.1042628,0.009291842],"study_design_scores_gemma":[0.002272778,0.001321136,0.4468185,0.00003205541,0.00003246896,0.0000303621,0.0005846413,0.001522482,0.1241152,0.0000627445,0.422419,0.000788594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825605,0.000007429049,0.0005840905,0.001668872,0.0001884772,0.0008608855,0.01410592,0.00001290876,0.00001089369],"genre_scores_gemma":[0.9936941,0.000006598428,0.002484825,0.001648978,0.00004978987,0.0000038854,0.00191964,0.00002330271,0.0001689137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4086692,"threshold_uncertainty_score":0.9579354,"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."}}