{"id":"W2484568918","doi":"","title":"PPIの日本と海外(特に米国)の動向について(総説)","year":2006,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003208363,0.0003915311,0.0004075522,0.0001890389,0.0001715718,0.00002875936,0.0005269662,0.0004358293,0.004723868],"category_scores_gemma":[0.00005416642,0.0004034801,0.0001423185,0.000344863,0.0003507728,0.0002112977,0.0000831346,0.0009471992,0.001335531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007402131,"about_ca_system_score_gemma":0.0000692785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002513591,"about_ca_topic_score_gemma":0.00005082816,"domain_scores_codex":[0.9978937,0.00005993628,0.0005226299,0.0003963016,0.0003713625,0.0007560613],"domain_scores_gemma":[0.9991354,0.0001208927,0.00005604393,0.0004720178,0.00003931185,0.0001763768],"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":[0.0001223885,0.0007190288,0.002574941,0.001465588,0.001054372,0.001823974,0.001820412,0.003301548,0.01847326,0.1555421,0.7739822,0.03912015],"study_design_scores_gemma":[0.004462391,0.0002688878,0.005473339,0.0004282709,0.0006568845,0.0002810039,0.001137254,0.03888109,0.02017769,0.05558008,0.8705804,0.002072762],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.190019,0.06778821,0.001423683,0.005238427,0.00491017,0.000492966,0.0001286672,0.002479786,0.7275191],"genre_scores_gemma":[0.993773,0.002033749,0.0005002597,0.0002013184,0.001136375,0.0000335661,0.00005774574,0.0000570226,0.00220691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.803754,"threshold_uncertainty_score":0.9998417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163000125039877,"score_gpt":0.2417318944621328,"score_spread":0.230101893211734,"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."}}