{"id":"W3409119","doi":"","title":"東日本大震災を風化させないために : 10年後を視野に入れた社会福祉の研究方法への提言 (特集 東日本大震災と社会福祉 : 「緊急時」と「平時」の支援から考えること)","year":2013,"lang":"en","type":"article","venue":"社会福祉研究","topic":"Maternal and Perinatal Health Interventions","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001336064,0.0002222673,0.0001301603,0.000482498,0.001734065,0.0008531851,0.000429948,0.0006278208,0.0438918],"category_scores_gemma":[0.001898193,0.0001899525,0.0002149351,0.0004542902,0.00117736,0.0005020378,0.0005263504,0.0004358036,0.00664049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006318668,"about_ca_system_score_gemma":0.005073835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2169501,"about_ca_topic_score_gemma":0.4379361,"domain_scores_codex":[0.9991599,0.0001689626,0.00008000058,0.00008366432,0.0002919055,0.0002155279],"domain_scores_gemma":[0.9987883,0.0001479851,0.0001297515,0.00002962972,0.0004194009,0.000485026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001243115,0.001307501,0.4277956,0.001207161,0.00005842053,0.001285656,0.02351936,0.0002436609,0.01018724,0.009373052,0.1937484,0.3300309],"study_design_scores_gemma":[0.0001681399,0.001150364,0.5212433,0.0003662907,0.00006555092,0.0007661308,0.008396237,0.0003148733,0.002962552,0.001732318,0.4627714,0.00006282922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6762099,0.002945607,0.0018598,0.01364817,0.0008302418,0.001491008,0.003642622,0.0001800248,0.2991926],"genre_scores_gemma":[0.7253325,0.002465855,0.006981706,0.002813434,0.0003160276,0.0007161112,0.002935081,0.000048451,0.2583908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2169501,"threshold_uncertainty_score":0.4313746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03328848276595564,"score_gpt":0.350056155370883,"score_spread":0.3167676726049274,"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."}}