{"id":"W3044753947","doi":"","title":"Assessing China's Merchandise Trade Data Using Mirror Statistics | Bulletin – December Quarter 2015","year":2015,"lang":"en","type":"article","venue":"Philadelphia Museum of Art Bulletin","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); China; Geography; Business; Archaeology","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":[0.001614985,0.0003758383,0.0003177592,0.003833427,0.0003812163,0.001341072,0.0002970525,0.0002540423,0.004591089],"category_scores_gemma":[0.008111746,0.0002599018,0.0002894726,0.00503286,0.0002586917,0.001171628,0.0006389198,0.0003613833,0.001518252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325489,"about_ca_system_score_gemma":0.002378616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1049447,"about_ca_topic_score_gemma":0.1265054,"domain_scores_codex":[0.9991375,0.00007432085,0.00009225101,0.00008354206,0.000544041,0.00006819622],"domain_scores_gemma":[0.9946868,0.0005042041,0.0008757802,0.0005317209,0.003116389,0.0002850383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001464362,0.00006980325,0.7923623,0.0001507301,0.0001433289,0.0001674375,0.0002503365,0.004099418,0.001479161,0.003166433,0.1169038,0.08106091],"study_design_scores_gemma":[0.00001137458,0.00005261907,0.9471119,0.00003410958,0.00004071286,0.00004109013,0.0002366947,0.006711604,0.001782308,0.0003880389,0.04356391,0.00002572331],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8504211,0.0007506073,0.003969558,0.001908633,0.0002758487,0.000160335,0.0983709,0.0008246737,0.04331833],"genre_scores_gemma":[0.8776627,0.0005819827,0.003236802,0.0001306538,0.0001130007,0.0000690286,0.09514607,0.0001243533,0.02293536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1049447,"threshold_uncertainty_score":0.2086678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2340983646734169,"score_gpt":0.3117229088949647,"score_spread":0.07762454422154783,"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."}}