{"id":"W6976747762","doi":"10.6068/dp14ba88790c352","title":"Trend 1992 - 2000. Statistics Canada. CANSIM: International Trade - Merchandise Exports | Country: Canada | Table: Merchandise imports and exports balance of payments and customs-based price and volume indexes for all countries | Variable: Customs, Wool and man-made fibres, imports, Price index, Paasche current 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":"Cultural Heritage Management and Preservation","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Balance of payments; Official statistics; Economic statistics; Census; Payment; Balance of trade; Balance (ability); Price index; Summary statistics; Statistical analysis","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":[0.001268622,0.002466741,0.002119045,0.008473821,0.002516257,0.004594884,0.004141564,0.001215,0.08618128],"category_scores_gemma":[0.01204489,0.001339009,0.001610668,0.03906903,0.0005954601,0.00233498,0.001920828,0.002648487,0.06782482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03140023,"about_ca_system_score_gemma":0.07557533,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.987536,"about_ca_topic_score_gemma":0.9854097,"domain_scores_codex":[0.9973393,0.0001354113,0.0002581066,0.0004215859,0.001199442,0.0006462169],"domain_scores_gemma":[0.9799872,0.0007506562,0.0007704807,0.0006571324,0.01695775,0.0008768347],"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":[0.00001876924,0.000004786177,0.0008018897,0.0001990759,0.00001535299,0.000006757578,0.0000148822,0.00009670109,0.000009663218,0.0002962459,0.9973602,0.00117574],"study_design_scores_gemma":[0.00009964513,0.000007424772,0.014879,0.0005976604,0.00004028266,0.00002244353,0.0003135448,0.000354443,0.0001656531,0.0004626941,0.9830005,0.00005661382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004440522,0.00003396193,0.00001356224,0.00005419239,0.00001595651,0.000006003568,0.9991007,0.00004653752,0.0006847919],"genre_scores_gemma":[0.0004179434,0.0001360033,0.0001569577,0.00005971549,0.000008442167,0.00004381588,0.9966835,0.00006384152,0.002429837],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08618128,"threshold_uncertainty_score":0.2883051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03552296609489759,"score_gpt":0.2437280060358671,"score_spread":0.2082050399409694,"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."}}