{"id":"W2749010120","doi":"","title":"DAWN JAPAN DIALOGUE 8；ケースバイケースで適したインスリン導入法を考える","year":2007,"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":"Political 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":[0.001219464,0.0004650524,0.0004933515,0.0003124902,0.0002136216,0.00002709727,0.0007009965,0.0006040867,0.0034026],"category_scores_gemma":[0.0002065809,0.0004801712,0.0001641473,0.0004827847,0.0005591558,0.0002436199,0.0001209089,0.001362704,0.001264214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000104382,"about_ca_system_score_gemma":0.00007994565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009499324,"about_ca_topic_score_gemma":0.0001044655,"domain_scores_codex":[0.9971744,0.0000557833,0.0006946044,0.0004769539,0.0004542654,0.001144027],"domain_scores_gemma":[0.9985043,0.0003202216,0.00007268609,0.0006001756,0.00005053815,0.0004520177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006300079,0.001275722,0.009081421,0.002702454,0.003151014,0.004266337,0.02329175,0.0008281164,0.05078315,0.1509759,0.3152109,0.4378032],"study_design_scores_gemma":[0.007854134,0.0007865838,0.01820264,0.0007767437,0.0009786349,0.0006560661,0.009055211,0.01450099,0.04274332,0.02504992,0.875527,0.003868778],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3699895,0.02942761,0.005298006,0.002877214,0.009223938,0.0006776079,0.0001196452,0.002496878,0.5798896],"genre_scores_gemma":[0.9942971,0.002623085,0.0005348388,0.0003833805,0.00119707,0.00001667983,0.0000609159,0.00006939743,0.0008174674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6243077,"threshold_uncertainty_score":0.999765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737087133844898,"score_gpt":0.2622165480620852,"score_spread":0.2448456767236362,"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."}}