{"id":"W3157304342","doi":"","title":"アンチセンスTissue factor(TF)でのラット腎虚血再潅流障害の制御","year":2003,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Tissue factor; Factor (programming language); Computer science; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000614587,0.0006936808,0.0003868857,0.0008243081,0.0006635007,0.001109185,0.0003042113,0.0005790976,0.01161999],"category_scores_gemma":[0.001391153,0.0001247731,0.0007260169,0.0006570698,0.0009602192,0.0006854195,0.0003068665,0.000756306,0.005038159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009248441,"about_ca_system_score_gemma":0.001394041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004903828,"about_ca_topic_score_gemma":0.001431035,"domain_scores_codex":[0.9996314,0.00003949689,0.00002486457,0.0001073528,0.0001015396,0.00009531863],"domain_scores_gemma":[0.9993184,0.0001519167,0.0001057788,0.00004423592,0.0002497061,0.0001300696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001811855,0.0002169819,0.008425117,0.0008514976,0.0001330646,0.0009659245,0.0006087648,0.0006535641,0.7330376,0.01986955,0.008565558,0.2248606],"study_design_scores_gemma":[0.000170203,0.003124721,0.02321257,0.0001918795,0.0002660513,0.004749279,0.00124145,0.001450331,0.6952083,0.01019471,0.2600813,0.0001093062],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7096999,0.05864368,0.07627898,0.004203141,0.004540706,0.0006139223,0.001633199,0.00109993,0.1432865],"genre_scores_gemma":[0.9023084,0.01335537,0.01890473,0.0008777963,0.0009093661,0.0001310418,0.001167264,0.0001095174,0.06223646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01161999,"threshold_uncertainty_score":0.03887272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02014163384332863,"score_gpt":0.2665347918277675,"score_spread":0.2463931579844389,"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."}}