{"id":"W408843087","doi":"","title":"海外会計News&Topics 負債測定における信用リスクの取扱い","year":2009,"lang":"ja","type":"article","venue":"Accounting","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"],"consensus_categories":[],"category_scores_codex":[0.0002561887,0.0003310356,0.0003346239,0.0001661103,0.0002350067,0.0001048005,0.0004309444,0.000492549,0.0002986315],"category_scores_gemma":[0.00008937749,0.0003688704,0.0001213718,0.0003602858,0.00006966527,0.0004950728,0.00004699659,0.0007184174,0.000415683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006018001,"about_ca_system_score_gemma":0.0000290124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004213256,"about_ca_topic_score_gemma":0.00002492449,"domain_scores_codex":[0.9983575,0.00001934051,0.0004260005,0.0003411572,0.0001811276,0.0006748569],"domain_scores_gemma":[0.9992638,0.00004616716,0.00006617553,0.0005118618,0.00005451365,0.0000575079],"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":[0.00004717746,0.000216925,0.009862576,0.0005142764,0.0003645039,0.0004029925,0.004351006,0.005626549,0.01026838,0.482175,0.02509699,0.4610736],"study_design_scores_gemma":[0.003338661,0.0009172575,0.1924329,0.001265466,0.0005867657,0.00033832,0.009640657,0.08858464,0.006797044,0.340712,0.3500385,0.005347818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5149124,0.01247713,0.0007489607,0.003664095,0.001514039,0.000253587,0.00001201295,0.001564082,0.4648536],"genre_scores_gemma":[0.9957783,0.0004609803,0.001488145,0.0004672658,0.0007468131,0.000003464025,0.000009641949,0.0000286073,0.001016741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4808659,"threshold_uncertainty_score":0.9998763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008192421874949073,"score_gpt":0.213183107493482,"score_spread":0.204990685618533,"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."}}