{"id":"W6957831183","doi":"10.6068/dp14ba84b553d96","title":"Trend 1992 - 2000. Statistics Canada. CANSIM: International Trade - Merchandise Exports | Country: Canada | Table: Merchandise imports and exports customs-based price indexes and United States trade, and Standard International Trade Classification (SITC revision 3) price indexes for all countries and United States | Variable: Printed matter, imports, United States, Laspeyres fixed weighted | Units: 1992=100, 1992-2000. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-130.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Price index; Official statistics; Census; Summary statistics; International comparisons; Statistical analysis; Descriptive statistics; Balance of trade; National accounts","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":[0.001252063,0.002530188,0.002317156,0.008795398,0.002335035,0.004703401,0.004188918,0.001334346,0.07677574],"category_scores_gemma":[0.01216638,0.001366881,0.001716455,0.04201353,0.00058809,0.00234634,0.001807293,0.002869401,0.05828682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02958016,"about_ca_system_score_gemma":0.07462506,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9847297,"about_ca_topic_score_gemma":0.9819292,"domain_scores_codex":[0.9973342,0.000136335,0.0002708779,0.0004145869,0.001235287,0.0006087375],"domain_scores_gemma":[0.9806333,0.0007333757,0.0008233719,0.0006212921,0.01635301,0.0008357422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000207924,0.00000536837,0.000903079,0.0002296964,0.00001880476,0.000007202345,0.0000130503,0.0001076376,0.000009100069,0.0003335084,0.9971648,0.001186945],"study_design_scores_gemma":[0.0001273035,0.000008823973,0.01712726,0.0006988517,0.00004863804,0.00002368181,0.0003108876,0.0003656952,0.0001671751,0.0005499712,0.9805117,0.00006008587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004355897,0.00003953364,0.00001212504,0.00005675518,0.00001853696,0.000006207718,0.9992016,0.00003779196,0.0005838473],"genre_scores_gemma":[0.0004467285,0.0001553346,0.0001478584,0.00006379455,0.00001044411,0.00004526624,0.9969289,0.00004979253,0.002151869],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07677574,"threshold_uncertainty_score":0.2568404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143382899570515,"score_gpt":0.2593730069996379,"score_spread":0.2379391780039328,"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."}}