{"id":"W2940711106","doi":"10.1186/s41256-019-0097-z","title":"Probing popular and political discourse on antimicrobial resistance in China","year":2019,"lang":"en","type":"article","venue":"Global Health Research and Policy","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ottawa Public Health; Hamilton Health Sciences; Centre for Global Health Research; McMaster University; York University","funders":"Ontario Ministry of Research, Innovation and Science; Canadian Institutes of Health Research; Norges Forskningsråd; Nankai University; Government of Ontario","keywords":"Public health; Government (linguistics); Public relations; China; Resistance (ecology); Political science; Thematic analysis; Politics; Medicine; Sociology; Qualitative research; Social science; Nursing; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0005542105,0.0001132482,0.0001565083,0.00005091836,0.0001560142,0.00004199888,0.0001121584,0.00005064858,0.00007741673],"category_scores_gemma":[0.00005278571,0.00009287168,0.00001427455,0.000317897,0.0005365069,0.0001265842,0.0002602254,0.0003012807,0.0002463847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006162638,"about_ca_system_score_gemma":0.00005885599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004519548,"about_ca_topic_score_gemma":0.0006350418,"domain_scores_codex":[0.9978384,0.0001629941,0.0001632686,0.0003459713,0.0003416516,0.001147722],"domain_scores_gemma":[0.9990543,0.00003162388,0.00002010135,0.0001427733,0.000001662837,0.0007495662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006207436,0.0009594848,0.6109586,0.0004925237,0.000008672657,0.00006987525,0.0006552641,0.00001822046,0.003050078,0.3480391,0.005767688,0.02935972],"study_design_scores_gemma":[0.0009097453,0.0004799482,0.9766835,0.0002195553,7.94204e-7,0.00001989759,0.00008758547,0.0000717393,0.0002468905,0.01591687,0.005212686,0.0001508164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9458365,0.0001352655,0.000001251004,0.02717844,0.00002355811,0.0003694814,0.00004555262,0.000008948534,0.02640095],"genre_scores_gemma":[0.9964292,0.0002763555,0.0001017346,0.00246484,0.00005375524,0.000001691506,0.000004265125,0.000006238924,0.0006618882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3657248,"threshold_uncertainty_score":0.6832235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0561411125912849,"score_gpt":0.4526916000739922,"score_spread":0.3965504874827073,"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."}}