{"id":"W2602459235","doi":"","title":"“より”質の高い血糖コントロールを目指して：基礎インスリンとリキシセナチドの併用療法","year":2014,"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":[0.0007164251,0.0004712042,0.0005415136,0.000210822,0.0002039078,0.00003546055,0.0007546137,0.0004966366,0.004992598],"category_scores_gemma":[0.0002602337,0.0004762491,0.0001665001,0.00034719,0.0004420841,0.0002369521,0.0001264524,0.001177164,0.001851786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006186477,"about_ca_system_score_gemma":0.00005952023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004190842,"about_ca_topic_score_gemma":0.00001491307,"domain_scores_codex":[0.9975069,0.0001361557,0.0005581201,0.0004989179,0.0004328813,0.0008670382],"domain_scores_gemma":[0.9985481,0.0002519426,0.000070186,0.0007124796,0.00005086103,0.0003664277],"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":[0.0001873954,0.0005786163,0.001434869,0.002541361,0.001640938,0.0004744294,0.006606714,0.002007743,0.01488786,0.2092413,0.4366895,0.3237093],"study_design_scores_gemma":[0.002788746,0.0003451383,0.0009218793,0.0003454174,0.0003428675,0.0001471434,0.001120111,0.09222991,0.005908775,0.0220214,0.8724929,0.00133571],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1177244,0.02289237,0.007992759,0.007629805,0.008695962,0.0005909,0.00009434676,0.003155089,0.8312243],"genre_scores_gemma":[0.9933178,0.002877728,0.0007771608,0.0006109137,0.001083565,0.00003703789,0.00002947839,0.00007493053,0.001191348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8755934,"threshold_uncertainty_score":0.9997689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336018377594647,"score_gpt":0.2487507868395274,"score_spread":0.2353906030635809,"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."}}