{"id":"W2991378322","doi":"","title":"Medical Scope：新たなサーベイランスによる百日咳対策の変化","year":2019,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Business; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00137781,0.0002507819,0.0002187473,0.001061552,0.001511498,0.003072396,0.0003965861,0.001092654,0.01880095],"category_scores_gemma":[0.003070967,0.0001791234,0.0003287475,0.0006440157,0.003119397,0.001857301,0.001245013,0.001338078,0.003723981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001453549,"about_ca_system_score_gemma":0.003217051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002351403,"about_ca_topic_score_gemma":0.002118499,"domain_scores_codex":[0.9991198,0.0002190447,0.00007096484,0.0001277574,0.0003753596,0.00008698112],"domain_scores_gemma":[0.9979486,0.0007466405,0.0002569969,0.0001225631,0.0005625253,0.0003626938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002056039,0.0003277493,0.0194128,0.0009238617,0.00007625655,0.001721298,0.007128628,0.001190153,0.009734219,0.4096532,0.03701019,0.512616],"study_design_scores_gemma":[0.00008549398,0.0005336665,0.0422434,0.001052164,0.0001446415,0.004545217,0.009363297,0.001316189,0.01081459,0.2172047,0.7125635,0.0001330955],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08452193,0.02128027,0.01671401,0.02775203,0.001479087,0.0002852015,0.000394019,0.0001548864,0.8474184],"genre_scores_gemma":[0.8065444,0.01836365,0.01501993,0.005095148,0.002377215,0.0002165321,0.0002419925,0.00005283275,0.1520882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01880095,"threshold_uncertainty_score":0.06289542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123130734711566,"score_gpt":0.2623126927252281,"score_spread":0.2499996192540715,"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."}}