{"id":"W3005097840","doi":"","title":"Medical Scope：蕁麻疹診療における心身医学の役割","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":[0.001598401,0.0002540985,0.0002187335,0.001200005,0.001396645,0.003076484,0.0004223845,0.000964339,0.008444712],"category_scores_gemma":[0.003254447,0.0002039887,0.0002854458,0.000621868,0.004815585,0.002300099,0.0009637718,0.001220542,0.001491884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001560882,"about_ca_system_score_gemma":0.002700889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001975817,"about_ca_topic_score_gemma":0.001779523,"domain_scores_codex":[0.9991279,0.0002329899,0.00006578003,0.0001438884,0.0003547594,0.00007464888],"domain_scores_gemma":[0.9975681,0.001073671,0.0003154157,0.0001568365,0.000597725,0.0002882976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001705149,0.0002924899,0.01715659,0.0008442885,0.00008906879,0.001358112,0.01089615,0.001904883,0.01537286,0.632201,0.009915838,0.3097982],"study_design_scores_gemma":[0.0001013066,0.000698106,0.04925852,0.0008934072,0.0002428527,0.003873339,0.01920848,0.003117263,0.02824726,0.581569,0.312606,0.0001843634],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1855888,0.01627879,0.03655615,0.02000105,0.0008426242,0.0003157837,0.000280414,0.0001497292,0.7399867],"genre_scores_gemma":[0.9296034,0.006332238,0.01515012,0.001533946,0.000703047,0.000125211,0.00006752695,0.0000250019,0.04645956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008444712,"threshold_uncertainty_score":0.0282504,"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."}}