{"id":"W3202293498","doi":"","title":"重篤な凍傷後の再潅流:初回成功血栓溶解後の最初の完全厚壊死【JST・京大機械翻訳】","year":2020,"lang":"ja","type":"article","venue":"Plastic Surgery Case Studies","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":[],"category_scores_codex":[0.0003401936,0.0008002113,0.001461084,0.0002737348,0.0004708683,0.00005083994,0.0002169127,0.0003796783,0.0003929321],"category_scores_gemma":[0.005690525,0.0007985924,0.0003860682,0.0008026185,0.0005841946,0.0002704248,0.0002503422,0.0008283869,0.000837178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009904944,"about_ca_system_score_gemma":0.00009778869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000324316,"about_ca_topic_score_gemma":0.00006867402,"domain_scores_codex":[0.996836,0.0001295997,0.0009543896,0.0007699773,0.0003046937,0.001005354],"domain_scores_gemma":[0.9895309,0.009444279,0.0001221913,0.0004398199,0.0001573038,0.0003055282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0009113417,0.0004728756,0.0169688,0.01171821,0.01692483,0.3285947,0.03627172,0.02188046,0.001203344,0.0255681,0.5250084,0.01447709],"study_design_scores_gemma":[0.008907191,0.004174869,0.01362919,0.008100646,0.0119399,0.06697939,0.5098585,0.157375,0.008435569,0.02880748,0.1555479,0.02624433],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8631468,0.1105292,0.0009619234,0.002833637,0.01064839,0.0003977715,0.0004312384,0.002349593,0.008701491],"genre_scores_gemma":[0.99152,0.007063978,0.0002034763,0.0002259859,0.0007351227,0.0000548971,0.0000169654,0.00008647556,0.0000930824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4735868,"threshold_uncertainty_score":0.9999408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04630194751786479,"score_gpt":0.2438805841033858,"score_spread":0.197578636585521,"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."}}