{"id":"W2359822578","doi":"","title":"Comparison on application of tempering justice with mercy in arrest censorship between countries(regions) of Anglo-American law system and our country","year":2008,"lang":"en","type":"article","venue":"Journal of Shenyang Institute of Engineering","topic":"Criminal Law and Evidence","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Censorship; Economic Justice; Law; Political science; Tempering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005036409,0.0000990927,0.0004560124,0.0001481649,0.00008182244,0.000009279695,0.0001709512,0.00004794116,2.550683e-7],"category_scores_gemma":[0.00009387518,0.00008926296,0.0000356898,0.0003122065,0.0002526921,0.0003096407,0.00001369449,0.0001702024,2.227983e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118412,"about_ca_system_score_gemma":0.000100325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002658111,"about_ca_topic_score_gemma":0.000421424,"domain_scores_codex":[0.9988034,0.00002622758,0.0005164074,0.00008903415,0.0004158043,0.0001491576],"domain_scores_gemma":[0.9990085,0.0001213378,0.00051246,0.00009755034,0.0001779679,0.00008221138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006855638,0.0002873428,0.4297527,0.005188961,0.0002553356,0.0001388584,0.02171664,0.2433598,0.008615739,0.2889815,0.0001022702,0.0009153184],"study_design_scores_gemma":[0.005158164,0.003288078,0.7502248,0.02631087,0.001775832,0.000380004,0.1065222,0.008481088,0.02398556,0.00006214336,0.07186966,0.001941565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959112,0.0002700274,0.002857897,0.0001532332,0.0001528935,0.0001055314,0.000005550246,0.00001021654,0.0005334539],"genre_scores_gemma":[0.9980429,0.0001350872,0.001641409,0.000005226815,0.0001627002,0.000001530876,6.665131e-7,0.000008117725,0.000002372537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3204721,"threshold_uncertainty_score":0.4018286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03578385648619078,"score_gpt":0.3096061267926808,"score_spread":0.27382227030649,"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."}}